• Hot Hitters:  This has hitting stats for the past 20 days of all hitters with 1+ AB.  Besides your typical 5×5 stats, it includes BABIP, OBP, OPS, SLG, and K%.  Players are ranked in descending order of their ‘Heat Index’ which is calculated using a mix of Hits, Runs, HRs, RBI, SBs, and ABs.  Each player is indexed against the 156th most valuable hitter for the period (156 hitters are active on a given day in a 12-team league).  While it isn’t possible for us to know if he’s available or not in your league, we provide pre-season and in-season $ estimates (based on 12 team 5×5 ESPN rosters) – the higher those values, the more likely a player is already owned.
• Hot Pitchers:  This has pitching stats for the past 20 days of all pitchers with 0.3 IP.  Besides your typical 5×5 stats, this includes FIP, Blown Saves, Holds, K/BB, K/9, and Quality Starts.  Players are ranked in descending order of their ‘Heat Index’ which is calculated using a mix of Wins, Saves, Earned Runs, Hits, Walks, Innings, and Holds.  Each pitcher is indexed against the 108th most valuable pitcher for the period (108 pitchers are active on a given day in a 12-team league).  Pre-season and in-season $ estimates (based on 12 team 5×5 ESPN rosters) are provided for each player to help identify players more likely to be free agents in your league.

The inaugural #1 hot hitters/pitchers are Carlos Gonzalez (edging out Josh Hamilton) and Johnny Cueto (edging out CC Sabathia).

As with our other Player Raters, the grids are sortable and you can filter based on position (including SP vs. RP).  The data should update every weekday by around 10AM EST – the last column will indicate the last day’s worth of games that are included.

If you see a player missing or set at the wrong position, please mention it in the comments section of the latest post.  Please note that players just called up may take a week or so to be added.

The player rater work is an adaptation of the Point Shares methodology I’ve used the last couple of years for pre-season and post-season player estimates.  Here is a link to a favorable test I did earlier this year vs. ESPN’s player rater methodology.  After some trial and error plus assistance from a variety of folks (Eric K at my favorite fantasy baseball escort service – EliteFantasyPlayers.com - and Doug at Dougstats.com among others), we now have a fairly automated system for updating in-season player rankings on a daily basis.

The Razzball Player rater is at –> Fantasy Baseball Player Rater and covers the following 5×5 league formats:

• ESPN Roster format (C/1B/2B/SS/3B/5 OF/CI/MI/UTIL/9 P) – 10 Team / 12 Team / 14 Team / 15 Team / 16 Team MLB
• Yahoo! Roster format (C/1B/2B/SS/3B/3 OF/2 UTIL/2 SP/2 RP/4 P) – 10 Team / 12 Team / 14 Team / 15 Team / 16 Team MLB
• AL-Only (2 C/1B/2B/SS/3B/5 OF/CI/MI/UTIL/9 P) – 10 Team / 12 Team
• NL-Only (2 C/1B/2B/SS/3B/5 OF/CI/MI/UTIL/9 P) – 10 Team / 12 Team

The table is ranked based on a players’ projected Point Shares (a player’s value in standings points vs. the average player with some factoring in of position).  Dollar estimates are provided both for in-season as well as comparisons vs. pre-season estimates.  Take the dollar estimates with a grain of salt for now – they should become more stable as the season goes on.  You can filter by position (P for all pitchers, -P for all hitters) and sort by any of the columns (1 click ascending, two clicks descending).

There are two pages focused on popular hitter/pitcher stats outside of 5×5 (popularity based on our pre-season poll results).  These tables are filterable/sortable as well.

• Hitting – OBP, SLG, OPS, Hits, Total Bases
• Pitching – Quality Starts, Holds, Losses

Lastly, there are two tables that highlight differences between pitcher FIP vs. ERA and hitter BABIP vs AVG.

• The pitcher table is sorted based on the ‘luckiest’ pitchers – i.e. pitchers ranked in descending order based on the difference between their FIP and their ERA.  For those wondering why I chose FIP vs. xFIP, I do not have access to the league-average fly ball to home run ratio nor pitcher HR:FB ratio.  You may also find that my FIP estimates are slightly off from other sources – this is mainly because I cannot currently separate out intentional from non-intentional walks but it can also be due to how the ‘constant’ is applied to bring the league average FIP in the 3.20 range.
• The hitter table is sorted based on the ‘luckiest’ hitters - i.e. hitters ranked in descending order based on the difference between their current AVG and their expected AVG.  A hitter’s expected AVG is calculated by applying a hitter’s 3-year BABIP to their in-season performance.  3-year BABIP was used as this stat does vary per hitter based on various factors (line drive rate, their speed, GB to FB ratio, etc.) but a hitter’s BABIP tends to be steady in the long run.  Hitters with less than 100 AB in the previous 3 years are given the league average BABIP of .300.

I’ll do my best to keep these tables updated daily (generally by 10 AM EST).  The last column of each chart reflects the last games included so it will be transparent when it has not been updated for a couple days.  While I will do my best to keep on top of the moving pieces, please do not hesitate to provide the following information in the comments section of Grey and/or my posts:

• Any missing players from the tables (for now, I’m including any hitter/pitcher with 1+ AB or 0.1+ IP.  That minimum threshold will likely increase as the year goes on.
• Any position eligibility changes based on 10 game in-season eligibility (I know Yahoo! is 5 games but prefer to make one change across both).  For hitters where position eligibility seems imminent (e.g., Jesus Montero at catcher), I include the additional position and add an asterisk at the end of it.
• Any wonky data or functionality

Other potential FAQ:

• Will you ever have a ‘rest of season’ player rater?
• Maybe.  Would be dependent on a respected projection source providing an uploadable file that is 1) updated on a regular basis, 2) accounts for expected playing time, and 3) is free.
• Will you create a dynamic player rater to reflect any conceivable league format?
• Not planning on it.
• Was your wife turned on by this accomplishment?
• Nope.  She prefers it when I go Don Draper and fix shit around the house in a white t-shirt.

Anyway, we’ve done some informal comparisons in the past w/ ESPN but – after having a prolonged, dorktastic debate – I decided to take it one step further.  I created a test where I pitted our Point Shares against two other player raters:  ESPN and RotoTimes.

The test went like this:  Create 10 random 10 team leagues and calculate the standings using the real stats.  Then do the same using the rating points for each of the services (so if you add up the ESPN HR player rater points for each team, the one with the most points gets a 10 in HR).  The team point totals calculated by the player raters is compared to the point totals from using the real stats and we added up all the differences (so if the real points total was 70 and the player rater projected 68, it’s worth 2 points).  The lower the point total the better.

Before we go into the results, it’s worth noting that this test puts our Point Shares at a disadvantage.  Why?  Our methodology factors in position and projected team standings so that you can use it to directly estimate the impact of a trade or draft pick.  We adjust our ratings by crediting points against the average found at the player’s position instead of the average hitter.  All stats equal, we know that a catcher will be much more valuable than a 1st baseman (why else would people draft Russ Martin and V-Mart in the 3rd round last year?).  Geovany Soto and Justin Morneau both hit 23 HRs.  While RotoTimes and ESPN ignore position and credit each with the same number of points, we have a 0.5 difference.  This is saying that – assuming you fill the rest of the rosters up with average players – owning Soto will give you a 0.5 advantage in HR points vs. owning Morneau in a 10-team league.

But a test where you just add up rosters takes this out of the equation.  You could conceivably be perfect just by creating a ‘rating point’ completely proportional to the real total.  So 20 vs. 30 HRs could be worth .2 and .3 points or 2 and 3 points, etc.  Will those numbers help determine the value of 20 vs. 30 HRs?  Not really.

Okay, with that said, the results of the test are as follows (if you want to see the spreadsheet, click here – warning: it’s messy).  Out of 100 teams, the total points off by the Player Raters was:

RotoTimes – 112
Razzball Point Shares – 143
ESPN – 152

Our Point Shares were the most accurate for 3 of the 10 leagues while RotoTimes claimed at least a share of 1st in the other 7 (ESPN tied them 3 times).

At a category level, I performed correlation tests between the ‘real’ standings and those of the player raters.  100% would be perfect.

As you can see, Point Shares held up pretty well to ESPN and RotoTimes despite taking on the extra burden of factoring in position and tying it to expected point gains/losses in the standings.  The test did shine light on some improvements we can make on pitcher counting stats (W, SV, K) – note the higher correlation we have on ERA/WHIP because we can directly tie it to IP.

Final summary:

1) RotoTimes is the most accurate of the three.  Combined with the facts that you can both customize the rankings based on league size, categories, roster size, etc. and they provide $ estimates, we’d say this is the best pure ranking tool.

2) Point Shares have proven to be in the same league as these other player raters.  Given the extra utility of Point Shares because it directly estimates the impact on team points of player moves, we’d say our Point Shares are the most useful of the three.

3) ESPN, while not the ‘testwide leader’, did pretty well – especially in hitting stats.  Their biggest pure weakness is ERA/WHIP as it would appear that they don’t factor in IP like Razzball (and, we assume, RotoTimes).  But we’d say their biggest total weakness is that their player points mean ABSOLUTELY NOTHING.  They are arbitrary numbers that could be used to rank players but not to estimate their value in the standings or worth at the draft table.  They do a good job, though, at keeping it updated throughout the year…

A ‘point share’ is the estimated impact on a team’s points by substituting a player for the average drafted player at his position on a team filled with average players.  So in a 10 team league, this team would otherwise earn 5.5 points per category (55 points).  Substituting Cliff Lee for Zack Greinke on an average roster would mean an average of 6.6 points (6.40 for Cliff Lee, -0.24 for Greinke).

Our 2008 Player Rater that is based on a 10 team MLB league with 5×5 scoring.  Only the top 50 are displayed below the post.

We will use this as the foundation for future articles but here are answers to some anticipated reactions:

1) How could pitchers be in the top 4 slots?

It might be surprising to see starting pitchers in the first 4 slots.  This isn’t a flaw in the ratings- it’s a flaw in your thinking (sorry).  The best pitchers in a season generally dominate 4 categories (W, K, ERA, WHIP).  While they don’t contribute in Saves, there is little opportunity cost to this since saves are concentrated in 30-40 players and there are 90 pitching roster spots.  For hitters, it’s rare for a player to be excellent across more than 3 categories and there’s a larger opportunity cost for the categories they are not great (i.e., OFs like Ichiro and Taveras hurt HR/RBI).  Only 6 hitters in 2008 ranked above average across all 5 categories – Pujols, Wright, Berkman, Holliday, A-Rod, and Braun.  Hanley Ramirez had a great year in Runs (125), HR (33), and SB (35) but was mildly valuable in AVG (.301 or .006 less than Ryan Theriot) and negative in RBI (67 or tied for 6th among SS).  Converting Roy Halladay’s stats to an OF (W=R, HR=SO, RBI=WHIP, SB=SV, AVG=ERA) would net 119/34/121/10/.335.  Sabathia, Lincecum, and Cliff Lee weren’t far off this.  The reason why it’s usually not advisable to draft a pitcher in the 1st round is the unpredictability of who’ll be the top pitchers (none of the top 4 were top 50 picks based on MockDraftCentral’s Average Draft Position) vs. their true value.  Here’s a more thorough explanation of a pitcher’s fantasy value.

2) Why are there are only 110 players that have positive value when there are 230 open roster spots?

In Point Shares, 0.0 represents an average player at their position.  So it is expected that about 1/2 the eligible players are above average, 1/2 are below.  A negative score doesn’t mean that a player isn’t worth having on your roster – it just means that he’s below average.  If the system was built with 0.0 representing a roster-worthy player, the points would then represent the value of a player on a team in last place.  This overestimates the value of a player when measuring the impact of a player swap (e.g., Lee for Greinke) for every instance except when a team actually did finish last.  The best approximation of player value is starting from the middle.

3) What is the highest/lowest score possible?

Theoretically, the best possible score should be 4.5 pts in each category (moving someone from 5.5 to 10) or 22.5 for a 10 team league.  Any other league size, just divide the team total by 2 and subtract 0.5 (12 team = 5.5).  A simpler way of looking at it would be to consider how one would calculate the average value per team of a player with 50 SBs.  For the team that’s 1st in SBs, his value is 0.  For the 2nd team, it could be worth +1.  Take this to its logical conclusion and you get (0+1+2+3+4+5+6+7+8+9)/10.  That’s 45/10 = 4.5.

While no hitting scores fall above 4.5 or below 4.5 (closest is Willy Taveras’s 4.3 in SB), the Point Shares system is just a model so it is conceivable that an extraordinary performance would net more than 4.5.  The only cases in 2008 were extraordinary bad ERAs and WHIPs.   (You’d think K-Rod’s 62 SV but that only netted +3.9).

Assuming you have a completely average team (55 points in a 5×5 10 team league), each player’s point shares reflect how many points they would add/subtract from your team’s totals if they replaced the average stat projections at that position. For example, if you swapped out an average team’s 1B (Mark Teixeira, David Ortiz) with Lance Berkman, they would increase from 55 points to an estimated 67.5 points. Point Shares also allow for basic trade comparisons (add the points of each side) and assessing your team vs. others (just add up the points and add to 55).

Seeing that we’re 2 months into the season, we thought it was a good time to update our Point Shares rankings – in this case, it is based solely on actual stats through June 8, 2008. So this is more comparable to the ESPN Player Rater vs. ESPN’s player rankings or Yahoo’s Big Board. Except better. (Not saying there aren’t flaws in ours but, c’mon, Ichiro is #17 among all players with a .290/43/3/17/26? At OF?)

June 8th – Point Shares for 5×5 10 team league and for Fantasy Razzball (play for worst team)

(Click on images to enlarge)

Pre-Season – Projected Point Shares for 5×5 10 team and 12 team leagues

Pre-Season – Projected Fantasy Razzball Point Shares

Below are the links to these posts. Interesting reading if you’re a fantasy baseball or stat junkie. Much less interesting than smack if you are just a junkie.

Part 1: How Valid Is The ESPN Player Rater?

Part 2: Are Fantasy Baseball Pitchers Correctly Valued By Player Raters?

Part 3: How Do You Value Fantasy Baseball Hitters?

The whole journey to prove/disprove the ESPN Player Rater eventually led me to the Point Shares concept that is the foundation of my 2008 drafting and player valuation.

Anyway, I checked in on the ESPN Player Rater in 2008 and it looks like they listened to good ol’ Rudy. Some of the improvement I’m seeing:

• Negative values (particularly in AVG, ERA, WHIP) – Where 2007′s version would have a floor at 0, the current rater penalizes very bad performance. CC Sabathia currently sports -3.31 points in ERA and -2.64 in WHIP.
• No arbitrary cap at 5 points per category – I’m seeing some category leaders at 3 and Michael Bourn at a 7.64 for SB.

It’s too early to do a full assessment of the methodology. I figure I’ll do a Point Shares update at the All-Star Break and compare it then. But given the shit I gave ESPN during the offseason, it’s only fair that I give them credit for coming to their senses (and listening to me).

Has anyone in Razzball Nation been using ESPN for their fantasy league? If so, post your feedback! Grey and I went cold turkey on ESPN FLB after last year’s debacle (first two weeks of stats were fried – I had to calculate the data offline and make year-end adjustments). But I thought their new platform was pretty good. Interesting to hear what the rest of y’all think.

Oh, and just in case the worldwide leader is listening, add smart people to Baseball Tonight. Replace Eric ‘The Emmitt Smith of ESPN Baseball” Young and John Kruk with guys from ESPN.com like Keith Law and Rob Neyer. If you must pander to the masses, go with some tail like Erin Andrews.

Also, do you have the replacement level averages you used for the point shares handy?

Rudy’s Response:
Regarding Dunn, here are the baseline projections for us and Baseball HQ:

Razzball: 513 AB / 100 R / 38 HR / 100 RBI / 7 SB / .256
Baseball HQ: 553 AB / 105 R / 41 HR / 104 RBI / 7 SB / .251

Can’t say that I’m familiar w/ Baseball HQ’s Player Rater (have the book and access to online projections) but I’m looking in the book and Dunn is drafted 62nd overall in their 15 team mixed league snake draft and tied for about 52nd place overall in projected dollar value at $24. Our Point Shares project him at #40. Blogmate Grey ranks Dunn at #33 in his top 100.

I think if you took Baseball HQ’s ranks and put them next to ours, you would find more dramatic differences than this. I see them having Pierre in the top 30 where he barely cracks our top 100 b/c we dock him for his crap HR/RBI (on a side note, I ended up with Dunn and Pierre in my expert league).

Here is one thought for the differences and a reason that compelled me to do Point Shares. Sites like Baseball HQ and BP tend to be snobs on fantasy league structures. They prefer single league auctions or – in the case of Baseball HQ’s simulated snake draft – a 15 team league (which I’ve never seen). I think the majority of players are in 10 or 12 team mixed leagues so the rankings have to be filtered through the scarcity/needs of that universe.

I think 10-12 team mixed leagues aren’t as starved for speed and middle infielders as the leagues that a Baseball HQ projects for. Pierre went in the 9th round of my 12 team expert league and no one was patting me on the back for great value (including myself).

Let’s dig into Dunn a bit more. While no one loves Dunn, look at where his projections rank him in his position:
1) Projected at #1 for OF HR (38)
2) Projected at #3 for OF Runs (100)
3) Projected at #4 for OF RBI (100)

Compared to the other power positions (1B/3B), the average OF has lesser power numbers: 78/20/77 vs. 1B’s 85/28/95 and 3B’s 85/25/90. So our point shares system likes Dunn a lot for those three categories. It would value him less if he was a 1B/3B, more if he played 2B/SS/C.

Now if he hit .300, I think he’d go in the 2nd round. He’s projected to hit in the .250 – .260 range. Not good since we’ve got the average 12 team BA at .282. His walks help because he’s slated only for 500-550 AB vs. 600-650 AB. The Point Shares takes all this into account and basically says that his crappy average neutralizes his Runs or RBI advantage (BA is -1.5, RBI is +1.5). So an OF hitting .260 with 100 RBIs and an OF hitting .282 with 77 RBIs are worth the same points on an average team (note: for comparison’s sake, universally loved OFs like Granderson and Sizemore are set to hit about 77 RBIs and only hit .275. their RBI + AVG point shares equal 0 and -0.4 respectively)

So Dunn’s remaining two category advantage and not awful 7 SBs still give him overall positive value. I’d take the Point Shares rank at face value and say he’s worthy of a 4th round pick in a 10 team or 12 team league – particularly if a team drafted speed or a pitcher in the first three and needs to compensate on power. His batting average is a drag but it’s an overrated drag. Yes, a switch to Ichiro would give you an estimated swing of 3.6 points in AVG but you’d also lose 3 points in RBI and 4.2 points in HR (surprisingly, SBs don’t make up this dramatic difference…hence, Ichiro barely makes the top 100 in player rater and is our consensus worst 2nd round pick).

Not sure if this fully answered your question but hope it shed more light on why Dunn does well on Point Shares and why he might end up on more than one of my teams.

Regarding replacement value, I decided to stop using them with our 2008 Player Rater and stick to position averages (making average player stat = 0 vs. replacement player stat = 0). My reasoning is that replacement player stats regress to a similar mean, exacerbated by disproportionate slotting of 1B/3B/OF in the UTIL slot. So the replacement player at every position looks about the same. I think this is misleading as a 1B/3B/OF with that average stat line hurts your team vs. competition more than a 2B/SS/C. By using average, it gives you a better indication that Figgins KILLS a team at 3B (think of the HR/RBI difference vs. teams w/ A-Rod, Wright, Braun, Cabrera, A-Ram, and Atkins) but is pretty valuable as a 2B.

The blogs set to participate are:
Razzball.com (Rudy Gamble)
Razzball.com (Grey Albright)
RotoProfessor.com
Greener on the Other Side
Mop Up Duty
Herb Urban
Cards in the Attic
Lou Poulas (Fantasy Insider Online, Sweet Lou’s Baseball Lab, Razzball.com)

We need two more participants so if you’re interested in joining, please e-mail us at info@razzball.com. Draft day is scheduled for Saturday, March 22nd at 4PM EST.

Check out these links for the rules and a review of the least valuable – or as we like to say the most invaluable – players in 2007.

The biggest thrill and challenge is the absence of established strategies and expert rankings. A-Rod is almost a universal #1 in standard games – who will be #1 in our “Nick Punto is Ford Tough” Razzball league? I have no idea though I would’ve guessed league poster child Nick Punto if he was assured of playing time.

To create somewhat of a level playing field, I’ve created a 2008 Fantasy Razzball Player Rater based on Baseball Prospectus and Baseball HQ projections. I adopted our Point Shares methodology that we used for projected rankings of standard fantasy baseball. I used Baseball Prospectus’s depth chart to make sure only projected starters or key backups were included in the rankings for position players and starting pitchers.

This Player Rater should help all the participants set up their draft rankings and, for those of you not playing, should prove to be somewhat amusing….

While peers suggest I try other high-quality (and free) projections like CHONE, ZiPs, etc., I’ve put projection experimenting on hold to tackle a greater quest – one that could benefit our site’s loyal readers and the fortunate souls who get redirected here by a search engine.

The challenge is answering the question “How do you convert player projections into rankings?”  As once you’ve settled on your projections, there are several key pre-draft considerations that need to take place to ensure success:

1. Value of Player Based on Position Depth – e.g, how much does a player’s value increase/decrease based on the other available options for that position?
2. Value of Players in Different Positions – e.g., how much do you sacrifice on a player’s total stats because they play 2B vs. 1B
3. Value of a Player’s Stat Mix – e.g., how do you compare the value of 40/120/10 (HR/RBI/SB) vs. 15/75/40?
4. Value of Hitter vs Pitcher Stats – e.g., how do you compare A-Rod vs. Santana?
5. Value of a Player By You vs. Others – e.g., how long can you wait before picking a player?

(Note:  Risk and health are other key considerations but they ideally should be factored into the projections – i.e., Rich Harden shouldn’t be projected at 200 IP)

While a solution for the above factors appears complex, the concept behind how to do it is rather simple:  Convert all the statistics to the same metric (think money – it’s real easy to compare 10 dollars vs 15 Euro vs. 2000 Yen if you convert the Euro and Yen to dollars).  This is the underlying concept behind Bill James’ Win Shares.

So what metric makes the most sense for fantasy baseball?  Where real baseball success is measured in Wins, fantasy baseball success is measured in points.

Hence, “Point Shares”

Please click for our inaugural edition of Fantasy Baseball Point Shares for 2008.  I’m going to refrain from a drawn-out explanation of the methodology.  The important parts to understand are:

1. Point shares represent the estimated impact on a team’s points by substituting a player for the average drafted player at his position on a team filled with average players.  So in a 10 team league, this team would otherwise earn 5.5 points per category (55 points).  Substituting one of those average pitchers (approx. Ian Snell) for Johan Santana would net an approximated gain of 7.8 points to 62.8.
2. To account for a hitter’s value outside their position (The utility spot, the fact that a SS HR is worth the same as an OF HR), hitters receive 2/3 of points value based on their stats vs. the average drafted player in their position and 1/3 of points value based on the average drafted hitter.
3. Since pitching positions can be filled with starters or relievers, player value was adjusted.  Starting pitcher values are 75% based on average drafted starting pitcher, 25% on average drafted pitcher.  Relievers are 40% on average drafted reliever, 60% on average drafted pitcher.
4. Hitters are placed at their most valuable position where they are 20 games eligible.  Their rank/value at other positions they are eligible (down through expected eligibility like Ryan Braun in OF) is listed lower down in the spreadsheet.
5. Two versions are included:  a 10 team, 5×5, MLB universe, C/1B/2B/SS/3B/CI/MI/5 OF/Util/9P and a 12 team, 5×5, MLB Universe with 2C/1B/2B/SS/3B/CI/MI/5 OF/Util/9P.

As with any player ratings system – especially one this ambitious – the standard question would be “How do you test this?”.  The beauty of this methodology is it was relatively easy to test.  I took 7 drafts off of Mock Draft Central and calculated the rankings based on the underlying projections (weighted model of PECOTA and Shandler) and the Point Shares.

After making a few adjustments, the results of the test were very promising – Point Shares predicted total team points within +/- 2 point for 45 of the 70 teams.  Another 18 were predicted within +/- 5 points.  Only 1 team fell outside of +/- 7 points.

On a category-by-category basis, the Point Shares correlate well with the total team stats.  For the hitting stats, the team Point Shares correlated at 97+% with the total stats.  For pitchers, Saves, ERA, and WHIP correlated at 90+% while Wins and Strikeouts were at 90% aside from one league where the projections tanked.  Why did the pitching stats not do as well as the hitting stats?  It is because of the random mix of starters and relievers who – unlike hitters – have vastly different counting stats.   ERA and WHIP proved most successful because they could be weighted by innings pitched.

Look out for future posts referencing these Point Shares and probably make some tweaks along the way – especially if we get revised player projections.

We also want to state clearly that this is NOT a recommended draft ordering.  The main reason is that it doesn’t factor in the 5th pre-draft consideration mentioned earlier – the “Value of a Player By You vs. Others”.  Yes, I believe Peavy is worthy of a top 5 pick but if you can get him in the 2nd round or possibly the 3rd round, by all means wait.  Average Draft Position stats are the one piece of valuable information you can get from Yahoo!, ESPN, etc.  If you’re playing in an advanced league, you may want to use those on Mock Draft Central (requires subscription).

Also note that some of the differences aren’t statistically relevant.  If you like Jose Reyes at 3.65 over Ryan Howard at 3.72, go with your gut b/c it’s a virtual pick’em anyway.

So use this as your rankings base then make adjustments based on your preferences and your feel for your fellow drafters.  And good luck…

So what makes a player truly invaluable in Razzball? All it takes are two simple things – opportunity and poor performance. The challenge is finding those magical players who both underperform vs. their baseball brethren and keep their role for a significant percent of the season.

The Razzball 6×6 format aims to reflect this opportunity/underperformance balance. Common hitting stats R/HR/RBI/AVG where (like golf) your goal is to score lower than your opponents are complemented by ABs (an opportunity measure) and K’s (a measure that credits both opportunity and underperformance). To ensure incompetence is rewarded instead of inactivity, teams with less than 5200 team ABs (400 per roster spot) receive All-Star prorated stats for those missing ABs.

Common pitching stats K/ERA/WHIP reward pitchers who can’t miss bats and are complemented by Losses (the inversion of Wins), Innings Pitched (a measure of opportunity) and HRs allowed (like the hitter’s K, the ultimate measure of a failed AB). Gone for the purposes of the game are the niche stats – SBs and SVs – as it’s too easy not to compile them and their inversions (CS and Blown SV) do not happen frequently enough.

Here is a brief explanation behind our Razzball Player Rater methodology. If you have any questions, please post on the board:

We carried over our regular Player Rater methodology which credits players for their performance vs. the Best Available Option (BAO) – i.e., the best option on the free agent wire in a 10 team, MLB league. Points are awarded based on the difference between the player’s stats and that of the BAO for each category. These increments for Razzball were created using some fancy math and some less fancy trial and error. Definitions for each category are in the attached player rater.

Half a player’s stats are based on the BAO for their position and half for the BAO hitter/pitcher. This factors in position scarcity without overvaluing as, at the end of the day, a HR is a HR no matter who hit it on your team.

The hitter BAO stats for Razzball are remarkably similar to those we had for regular FLB. It’s as if the BAO serves as the top of a bell curve and Razzball and FLB draft on either side of it (with the exception of no-hit/good speed guys like Juan Pierre who are equally valuable in both formats albeit for different reasons). Note the Razzball BAOs factor in that slightly weaker hitting positions like C, 2B, and SS will dominate the UTIL category where 1B/OF usually do in FLB.

The pitcher BAO stats for Razzball are higher in ERA/WHIP but, otherwise, not that far off from FLB BAOs.

A last note is that if a player’s stats exceed the BAO’s in a category, the player receives negative points. Some Player Raters, like ESPN, have a floor of zero. Not in ours. So a guy like Mike Cameron will gain points in average and K’s but will give back those points in HRs and RBIs.

So without further ado, let’s look at the top 20 most invaluable Razzballers of 2007:

1. Nick Punto – 3B/SS – Minn
2. Scott Olsen – SP – Fla
3. Livan Hernandez – SP – Ariz
4. Woody Williams – SP – Hou
5. Casey Fossum – SP – TB
6. Adam Eaton – SP – Phi
7. Nook Logan – OF – Was
8. Mike Maroth – SP – Stl
9. Dontrelle Willis – SP – Fla
10. Jerry Owens – OF – CWS
11. Felipe Lopez – 2B/SS – Was
12. Jose Contreras – SP – CWS
13. Daniel Cabrera – SP – Bal
14. Marcus Giles – 2B – SD
15. Trot Nixon – OF – Cle
16. Jeff Weaver – SP – Sea
17. Alfredo Amezaga – OF/SS – Fla
18. Kyle Davies – SP – KC
19. Dave Roberts – OF – SF
20. Nelson Cruz – OF – Tex

Similar to our standard 5×5 Player Rater, pitchers dominate at the top. Seven of the first 10 and 11 of the top 20 are pitchers. The reason behind this is also similar to our regular Player Rater – when pitchers are good or bad, they tend to be good or bad across the board. Scott Olsen’s 5.81 ERA and 1.77 WHIP over 176.2 poorly pitched IP couldn’t have happened without a higher HR rate (29), a healthy number of losses (15) and a strikeout rate that’s only a hair above average (133 Ks). Only Ron Shandler’s $19 folly Nick Punto was able to put together a hitting season that consistently invaluable.

Let’s analyze pitchers and hitters separately to better understand invaluable performance:

Pitchers
Like in the best books/movies, the pitchers who resonate in Razzball are rarely one-dimensional villains. They aren’t purely bad – there’s always something about them that holds out promise of rehabilitation. Is it that they are young and have good arms (Scott Olsen, Adam Eaton, Daniel Cabrera, Kyle Davies)? Is it that they were once all star caliber pitchers (Livan Hernandez, Dontrelle Willis, Jose Contreras, Jeff Weaver)? Maybe they seem solidly mediocre like Mike Maroth? Whatever that promise may be, it serves the purpose of instilling faith in their manager to keep handing them the ball every 5 days.

The most common aspect across these pitchers is a tragic inability to keep baserunners off the base paths and touching home plate. The average ERA and WHIP among these 11 pitchers is a 5.82 ERA and a 1.61 ERA over about 160 IP.

Losses are an interesting stat to analyze. While 36 pitchers had 14+ wins in 2007, only 17 had 14+ losses. These top 11 Razzball pitchers represent 7 of them but they also have some pitchers with downright mediocre Loss totals. Let’s look at two factors that seem to play a role on a pitcher’s loss totals:

1) Their Team – The better their team, the more they get bailed out.
2) Their Home Park – The more hitter-friendly the park, the more likely they get bailed out (and the less egregious their pitching truly is)

Adam Eaton and Livan Hernandez are examples of good team / good hitting park. These two somehow managed to sport .500 records (10-10 and 11-11, respectively) with WHIPs at 1.60 or better. Eaton owes Rollins/Utley/Howard some cheese steaks. Livan’s 2007 season puts the defect in defector.

Mike Maroth had a good team (Tigers) / average park for most of his 20 starts and 7 relief appearances which could explain how he went 5-7 while sporting a ghastly 6.89 ERA/1.88 WHIP. The Cardinal trifecta of Mike Maroth, Kip Wells (17 Losses, 5.70/1.63) and Anthony Reyes (2-14, 6.04 ERA) proved once and for all that if there’s an honorary Razzball manager, it’s Tony Larussa (also see the 721 total ABs gifted to Aaron Miles and So Taguchi which netted 5 HRs).

Dontrelle Willis and Scott Olsen had bad teams / bad hitting parks and managed identical 10-15 records in 35 and 33 starts, respectively, showing racial equality exists when it comes to left-handed Marlin pitchers.

Woody Williams, Jose Contreras, and Daniel Cabrera had bad teams / good hitting parks and stacked up some awful W-L records: 8-15, 10-17, 9-18.

Based on this sample, I’d say finding a bad pitcher on a bad team is the most important thing for Losses. Home park might play a role for their ERA and WHIP but probably not a big factor for Losses.

HRs allowed, on the other hand, definitely skews on home park. Of this cheap baker’s dozen, the trio that topped 30 HRs allowed all played in the hitter parks (W. Williams, L. Hernandez, A. Eaton). Jose Contreras’s 21 HRs was the exception – a gift to the small-ball loving Ozzie Guillen.

Strikeouts and IP don’t have much in terms of interesting stories. Daniel Cabrera is probably the only plus-K guy in the group and he more than makes up for it by walking so many hitters. Throwing 175+ IP isn’t the only way to near the top of the Razzball Player Rater for a pitcher but it helps. Tampa Bay’s Casey Fossum – the one-time Red Sox ‘prospect’ – had a bucket list kind of year, cramming all sorts of experiences within his 76 innings that led to an eye-popping 7.70 ERA and 1.79 ERA.

Stepping away from the top 20, the lessons we see in the Player Rater when it comes to pitchers are going to sound like bizarro FLB advice:

1) Look for starters with bad WHIP, average to bad K rates, and, preferably, bad teams.
2) Look for some positive story that assures them some role security – whether it be they once were good, they have ‘potential’, they are ‘workhorses’, they are the best a team has, etc. No use picking a stinker who gets demoted after 3 bad starts.
3) For relievers, you really want to stick to pitchers on bad teams. This provides a boon to both Losses and, potentially, innings pitched. Amazing that the 6 most invaluable relievers pitched for either Tampa Bay or Baltimore. Generally avoid closers – even the bad ones – as they don’t pitch as many innings.

The net-net on pitchers – predicting a bad pitcher is even harder than predicting a good pitcher. Draft conservatively and keep an eye on the FA wire for the next worst thing.

Hitters
What makes a good Razzball hitter? If you had to boil it down to one thing it would be this: little power. Besides minimizing HRs, low power guys are kept out of the meat of the lineup which reduces R and RBI opportunities (leadoff hitters would be less Run challenged) Of the 7 hitters to make the top 20, only Felipe Lopez made an honest run at double digit HRs (9).

The value of low power guys’ minimal R/HR/RBI per AB goes beyond their stats alone. With a team minimum of 5200 AB, the more AB you receive from low power guys, the less you need to invest in guys with medium/high power. Despite hitting for a measly .245, Felipe Lopez’s greatest value was his 603 AB that could allow you to carry a platooning 300 AB 1B and OF and still average 400 AB per hitter.

But a team can’t be built on little power guys alone as they tend to hit for higher averages, are less likely to strike out, and may be AB-challenged b/c of benching or platooning. In fact, only 9 players were able to hit the pick-six – being worse than the BAO in all six hitting categories (first number is their total player rater rank):

1. Nick Punto – 3B/SS – Min
14. Marcus Giles – 2B – SD
21. Tony F. Pena – SS – KC
24. Josh Barfield – 2B – Cle
30. Craig Biggio – 2B – Hou
44. Bill Hall – OF/SS – Mil
48. Lyle Overbay – 1B – Tor
51. Gerald Laird – C – Tex
66. Tadahito Iguchi – Phi

Strikeouts are a particularly vexing category as the top K guys often have a lot of power. Brandon Inge was the only player in the top 10 in K’s who didn’t have 20 HRs. Outfielders like Jason Bay, Chris B. Young, Mike Cameron, and Jack Cust are invaluable Razzball players as their power is compensated by high K’s, generally low average, and above average ABs.

An interesting twist to Razzball is that multi-position players are more invaluable for their more offensive-minded position. Nick Punto on the hot corner beats Nick Punto at MI. Guys like Rich Aurilia warrant a look at 1B. Alfredo Amezaga is a find at OF (forget the SS eligibility). Darin Erstad is an embarrassment of riches with 1B and OF eligibility (luckily, Erstad’s valuable team spirit and punting skills don’t factor into Razzball.)

So here are some Razzball lessons we see in the Player Rater:

1) Invaluable Razzball hitters are more predictable than pitchers. Concentrate on hitters during the early parts of the draft.

2) Find high AB, low power guys – no matter what the position.

3) Avoid starters at key power positions like 1B, 3B, or 5th OF in favor of platoon players or multi-position eligibility players.

4) Consider job security/opportunity. Nook Logan is a Razzball god but how many ABs will he get?

5) Try to find positive K guys with somewhat minimized power potential – i.e., a power hitter in San Diego. At the very least, hopefully they contribute low AVG and ABs. It may pay to draft a R/HR/RBI killer like Adam Dunn or Dan Uggla and save them for bad pitching matchups or bad hitter parks – say when they go on an NL West road trip to SF, LAD, and SD.

6) Gamble on the young ones – top rookies in 2007 include Alex Gordon, Tony Pena, and Stephen Drew. Look at minor league K rate as Gordon and Drew were particularly invaluable in this category. Figure their average will be hurt because of their greenness. Even partial successes like Delmon Young are worth it – his 65/13/93/.288 earned him a #67 on the player rater because of the 127 Ks and 645 ABs.

If you’re interested in joining the inaugural Razzball league, please send an e-mail to info[at]razzball.com. Special preference to fellow bloggers.

In two previous articles (part 1 and part 2), we’ve laid out alternative views for judging the most valuable player in 2007 5×5 MLB fantasy baseball (we say Peavy) and for pitchers – using and abusing the ESPN Player Rater in the process.

In this article, we’re going to focus on valuing hitters. The questions we will tackle are:

1) What is the value of each hitting stat?
2) How does position depth/scarcity affect a player’s overall value?
3) How does our approach to hitter value compare against the ESPN Player Rater?

To download our player rankings for 2007, please click our 2007 Fantasy Baseball Player Rater. To view the ESPN Player Rater.

What is the value of each hitting stat?

Our approach towards valuing player stats is to look at two factors: 1) the difference between a player’s statistics and those provided by the best available option (BAO) on the free agent wire (which would take position depth/scarcity into account) and 2) the impact that stat difference might have on a league’s standings (ambivalent to position).

We’ll set position scarcity aside for a second to look at the composite stats for the BAO hitter in 2007: 67 Runs / 14 HR / 65 RBI / 6 SBs / .277. The closest player equivalent to these stats is Luis Gonzalez.

We used the final standings of our fantasy league to understand the impact of each statistic by looking at the standard deviations between teams’ totals. While it would be better if we had more league standings on which to base these standard deviations, we still feel this is superior to building ratios off team averages because it takes into account that some statistics have larger percentage gaps between teams vs. others. This is most evident when looking at HR vs. SB – while the average team in our league average 1.69 HRs to 1 SB and the BAO has a HR:SB ratio of 2.33:1, the observed impact on a team was actually 1:0.77 or that a HR has more value (not even counting the R/HR/RBI/AVG implications) to a team’s rank in the standings than an SB.

The ratio for these stats based on our analysis was: 3.3 Runs / 1 HR / 2.8 RBI / 1.3 SBs / .003 AVG. Points are credited based on these ratios (a point actually equals the above ratio * 4.6) after subtracting the BAO’s stats.

Okay, let’s do two comparisons to show this works in action.

Eric Byrnes (103 / 21 / 83 / 50 / .286) vs. Miguel Cabrera ( 91 / 34 / 119 / 2 / .320)

This is an interesting one as it asks that inevitable question – how much are SBs really worth? Is it worth the addition 48 SBs to sacrifice those HRs, RBIs, and AVG that Miguel Cabrera provides? Let’s look at the points comparison of R/HR/RBI/AVG:

Runs: Byrnes 2.4 to 1.5
HRs: Cabrera 3.95 to 1.55
RBIs: Cabrera 3.95 to 1.43
AVG: Cabrera 3.48 to 1.01

(Note: While it might not look right that Cabrera’s 34 HRs could be worth 2.5x that of Eric Byrnes 21 HRs, remember that the BAO provides 14 HRs. So this is really a comparison of 20 HRs (34-14) vs. 7 HRs.)

Counting just these stats, Cabrera is about twice as valuable as Eric Byrnes (12.89 to 6.39).

But Eric Byrnes’ 50 SBs is huge given the average team only had 162 SBs in our league. A total like this could let you dominate SBs or focus on non-speed guys when filling out other positions (say, taking Khalil Greene’s 27 HRs instead of J. Lugo’s 33 SBs).

Eric Byrnes’ 50 SBs equates to 7.25 points in our scale while Cabrera’s 2 SBs equate to negative 0.45 points because it’s less than the BAO would’ve provided (which is 6). So factoring in SBs, Eric Byrnes is the more valuable fantasy hitter (13.6 to 12.4). But if your team was set for SBs, trading Eric Byrnes for Miguel Cabrera would be a no-brainer.

Placido Polanco (105 / 9 / 67 / 7 / .341) vs. Dan Uggla (113 / 31 / 88 / 2 / .245)

This comparison focuses on Polanco’s AVG contribution vs. Uggla’s power contribution.

Runs: Uggla 2.65 to 2.12
HRs: Uggla 4.0 to -0.78
RBIs: Uggla 1.94 to 0.30
SBs: Polanco -0.05 to -0.86

(Note: These comparisons do factor in position scarcity – hence, Uggla’s 2 SBs receive more negative credit that M. Cabrera’s above since the 2B BAO steals more than the average player.)

Counting these stats, Uggla is well ahead at 7.73 to 1.59 points, with the biggest driver being his 31 HRs which are worth 4.78 points more than Polanco’s 9 HRs.

But those HRs come at a price. Uggla’s .245 average is well below the BAO average of .277 (actually 2B’s have higher AVG than other positions so it’s even worse – examples of high batting average marginal 2Bs include Orlando Hudson’s .294, Luis Castillo’s .301, and Ronnie Belliard’s .290). Combining that bad average with his above average AB total (632), Uggla’s average would drop the average team’s AVG by .004 vs. the BAO 2B. This earns him a negative 3.28.

On the other hand, Polanco’s .341 in 587 ABs is worth a positive 4.77 points – more, in fact, than Uggla’s 31 HRs. He’s worth about an extra .004 on your team average meaning that swapping these two creates a .008 swing, a more dramatic swing than the 22 HR difference.

So while Placido Polanco is a negative on a team’s HR and SBs (and just about BAO level on RBIs), his high AVG catapults him into being a more valuable fantasy baseball contributor (6.4 to 4.4). If Uggla could just get to something like a .275 average or steal 20-30 SBs, his HR/RBIs could help catapult him up the 2B rankings (even with the anchor-like AVG, he ended up 7th most valuable 2B, well ahead of the .317 hitting ROY Dustin Pedroia).

How does position depth/scarcity affect a player’s overall value?

Position depth/scarcity plays a role from draft day through the end of the season.

During draft day, position depth/scarcity can increase/decrease a player’s value. A common practice is to ‘tier’ players at each position and try to group together similarly valued players. If there is only one player left in, say, the 2B tier and 5 similar valued players at SS, you may increase that 2B’s draft value because you can wait a round and likely get one of those shortstops.

After the draft, position depth/scarcity is used to compare the marginal benefit/loss of trading or adding/dropping one player over the next – e.g., I could trade Placido Polanco and replace him with little to no dropoff in any stat except AVG.

To factor this into our analysis, we extended our Best Available Option (BAO) concept to each position. We started with 10 rostered players for catchers and infield positions and 50 outfielders. We split the 1B/3B and 2B/SS positions equally and then divided up the utility position based on instinct and position depth (30% 1B, 2.5% 2B, 2.5% SS, 5% 3B, 0% C, 40% OF, 20% DH). We created composite stats for BAOs at each position – so for catcher, we took the 11th best AVG, 11th best HRs, etc. We then credited point totals based on the BAO at the position (“Position Points”) and averaged them with our average hitter BAO (“Player Points”). (Note: Since team rankings are position-agnostic – you don’t get more credit if it’s a middle infielder who hits a HR – there is a need to balance position depth/scarcity with overall stats. To keep it simple, we weighted it 50/50).

Below are the BAO stats per position (R / HR / RBI / SB / AVG) and some close statistical fits:

C – 47 / 13 / 57 / 2 / 0.273 (Paul Lo Duca, Johnny Estrada, AJ Pierzynski)
1B – 63 / 18 / 68 / 1 / 0.279 (Matt Stairs, Conor Jackson, Aubrey Huff)
2B – 79 / 11 / 61 / 9 / 0.288 (Orlando Hudson, Brendan Harris, Mark DeRosa)
SS – 72 / 11/ 60 / 11 / 0.279 (Brendan Harris, Jack Wilson)
3B – 70 / 18 / 72 / 4 / 0.279 (Kevin Kouzmanoff, Mark Reynolds, Aubrey Huff)
OF – 67 / 14 / 65 / 6 / 0.273 (Luis Gonzalez, Austin Kearns, JD Drew)

The most interesting about these BAO totals is how relatively close they are. The corner positions have a slight advantage in power and the middle infield spots have a slight advantage for runs, SBs and average. Catchers are weakest – particularly in Runs as catchers play less games and are disproportionately hitting 6th to 9th (less run opportunities).

Perhaps most surprisingly, the OF position looks no better than the middle infield positions. Wouldn’t you expect OF was a deeper position than middle infield? Isn’t BJ Upton more valuable as a 2B than an OF? Short answer: not really.

Here’s why: You’ve got roughly 15 2B, 15 SS, and somewhere between 52-57 outfielders on league rosters (OF are often used for UTIL positions). Looking at MLB rosters, you have roughly 30 starting 2B, 30 starting SS, and 90 starting OFs. FLB rosters, thus, are cutting deeper into the percentage of starting OFs vs. 2B/SS.

In addition, 2B/SS have added some pop over the years. 29 middle infielders hit at least 12 HRs. Granted, some had bad averages (Bill Hall, Juan Uribe, Stephen Drew), but the perception of those positions being power-challenged is outdated. (What IS true, though, is that it’s rare to find a middle infielder with 30+ HR power).

Outfielders, on the other hand, aren’t that deep. Only about 55 hit 15 or more home runs and that includes some players that might be at other positions (Berkman, Upton, Stairs) and the weakest ones look an awful lot like Luis Gonzalez and Austin Kearns (the BAO matches).

So while we did factor position depth/scarcity into our analysis, it really didn’t play a major role for hitters. The greatest impact was at catcher where the troika of great catchers in 2007 (Jorge Posada, V-Mart, Russell Martin) got about a 2 point boost because the Catcher position was the weakest in terms of BAO.

So Hanley Ramirez and Jimmy Rollins had extremely valuable fantasy years but the fact they played SS really didn’t add any significant value (maybe +2-3%).

How does our approach to hitter value compare against the ESPN Player Rater?

ESPN has a much simpler approach for estimating hitter value than the approach we have described above. It creates a cap at 5 points and a floor at 0 points. 5 points are awarded to the MLB leader in the stat and then each other player’s total is divided into the leader’s total and then multiplied by 5 to get their total – e.g., A-Rod led with 54 homers. David Wright had 30. He received 30/54 (.556) * 5 = 2.78 points in HR. Average is done in a slightly more complex way but the lowest possible total is zero (even if the player’s average has negative value).

From a macro-perspective, this simplistic approach works fine. The top hitters are going to appear near the top, the okay hitters in the middle, the bad hitters on the bottom. At a micro-perspective, we think ESPN’s simplistic approach has greater flaws vs. our approach. These flaws are less for hitters than pitchers, though, as the greater issues arise around ratio/average based stats and pitchers have two (ERA, WHIP) vs. one for hitters (AVG).

In a previous article, we identified four issues with ESPN Player Rater for valuing pitchers

1) Capping High Points at 5
2) Positive Ratio/Average Contributions Are Undercredited
3) Negative Contributions Aren’t Penalized
4) Overcrediting of Slightly Above Average Performance

These four issues all play a role for valuing hitters but #2 and #3 are not as major an issue because ERA/WHIP are more polarizing than AVG. For example, even low value hitters may hit .290 but only a great starting pitcher can manage an ERA near 3.00 ERA.

An additional issue we’ve found is:

5) The league leader used as the points base distorts the distribution of points – While the leader in Runs and RBIs is relatively close to the other leaders (no one had, say, 200 Runs or RBIs), A-Rod’s 54 HRs and Reye’s otherworldly 78 SBs set a very high bar for 5 points. This creates odd situations where Eric Byrnes 50 SBs (tied for 4th in majors) is worth less in ESPN Player Points than his 103 Runs (outside the top 20) and Jimmy Rollins’ 30 HRs (tied for 20th) are worth less than his 94 RBIs (tied for 42nd).

Here is the assessment on a stat by stat basis:

Runs – Overcredits for all players. For above average performance, Issue #4 plays a role (the Best Available Option’s 67 Runs warrants 2.3 points). For below average performance, Issue #3 starts taking effect (less than 67 runs should warrant negative points). An additional issue throughout is that runs are so plentiful across players that the value of a run is less than other stats (A-Rod’s 143 runs warrant 4.95 points in our estimation vs. 8.29 for his 54 HRs)

Home Runs – Undercredits great performance like A-Rod and Fielder (Issue #1). Issue #5 plays a role in underestimating the value of everyone at 25+ Homers. Players between about 15-24 HRs are slightly inflated based on Issue #4. Anyone below the BAO average of 14 are overestimated based on Issue #3.

RBIs – Undercredits the great performances like A-Rod and Matt Holliday (Issue #1). Overcredits above average performance (Issue #4). Undercredits below average performance (Issue #3). Issue #4 affects more hitters than Issue #3 (which is limited to speedsters and some 2B/SS – examples are Reyes’s 57 RBIs and Pierre’s 41RBIs)

SBs – This is the category where Issues #1 and #5 play a huge role in underestimating SB value. We have Jose Reyes’ 78 SBs at a whopping 11.5 points – the most points awarded for any offensive category. Teammate David Wright’s 34 SBs earned him a respectable 4.8 points (equivalent to Holliday’s 36 HRs and Vlad’s 125 RBIs). This underestimation affects hitter values all the way down to about 10 SBs. Issue #3 plays a very minor role – greatest for 2B/SS as speed is most common in that category (Freddy Sanchez’s 0 SBs earned him a negative 1.19).

AVG – Issue #1 only affects the top 3 hitters as Magglio, Ichiro, and Matt Holliday’s averages warranted 6+ points in our ratings. Issue #2 plays a role for the rest of those with averages above .330. Issue #4 plays a role in overestimating the value of hitters lower than .330 but greater than BAO (e.g., Luis Gonzalez’s pedestrian .278 warrants 1.99 ESPN points where it should be worth zero). For low average hitters, Issue #3 plays a role in greatly overestimating their value as they should have negative value. Uggla’s aforementioned average of .245 gets .86 ESPN points compared to our -3.28 points.

Amazingly, though, the cumulative effect of these issues seems to have little bearing on the ranking of hitters. We agree with the top 10 OFs from ESPN Player Rater with slightly different ordering. The top 10 2B match down to the order. The differences play more of a role in total player rankings – below are some examples of players differently valued (Our Ranking, ESPN Ranking).

Eric Byrnes (25, 43)
Jorge Posada (58, 92)
Juan Pierre (78, 117)
Derek Jeter (86, 106)

It’s worth noting that almost every hitter is higher valued in our rankings vs. ESPN because ESPN overvalues pitchers out of the top 20 and this pushes down all the hitters.

So while we find faults in ESPN’s methodology, we can’t fault using ESPN Player Rater to understand hitter position rankings. It works surprisingly well for hitters given its simplistic approach – it’s possible that its flaws are a bigger issue as you move down the player rankings. That said, we would caution against using the combined hitter and pitcher rankings given the flaws we’ve seen with their valuing and ranking of pitchers.

If you’ve ever seen the ESPN Player Rater (or, for that matter, other quantitative player rankings for fantasy baseball), you’ve likely asked yourself:

How could there be so many starting pitchers at the top? (13 in top 20, 19 in top 30) Is that valid or just faulty weighting?

This looks even more peculiar when reviewing qualitative rankings (i.e., someone subjectively lists players) or reviewing pre-draft rankings.

Before creating our own Player Rater, our assumption was that the preponderance of starters in the top ranks of ESPN’s Player Rater was due to faulty methodology versus the true value of starters vs. hitters.

So it came as a mild surprise to find that 12 of our top 20 were starters (and we also agreed with ESPN that JJ Putz deserved top 20 inclusion). We were somewhat relieved when there were only 2 starters in the 21-30 ranks so our 14 out of the top 30 was less than ESPN.

But those were gut reactions. Now that we’ve gone through the exercise, is their truth to ESPN’s (and our) pitcher-heavy top of the rankings? Are they eerily prescient or is this a broken clock scenario?

[It's important to differentiate this exercise - the proper valuing of player statistics - versus the projection of future statistics that are done by folks like Baseball Prospectus and Ron Shandler and are used for drafting purposes. Their analysis has shown that projecting hitter stats is more accurate than pitchers stats which makes hitters less risky for drafting. While it's extremely rare to see top 10 draft results with more than 2 pitchers, this does NOT mean pitchers are less valuable. That is based on a risk/value assessment - our analysis just focuses on the 'value' part of the equation.]

We’re going to look at this as two separate subquestions: 2A) Should there be a lot of starters in the final season top 20? & 2B) Is ESPN’s ranking of starting pitchers correct?

For question 2A, let’s first look at the players in our top 20 as well as that of ESPN. We shared 19 of the same 20 players, albeit in different order. Ours includes David Ortiz at #19 while they have Cole Hamels included at #20. (Still, agreeing so much with ESPN feels unclean.)

Our Ranking. Name – Pos (ESPN Ranking)

1. Jake Peavy – SP (2)

2. Alex Rodriguez – 3B (1)

3. C.C. Sabathia – SP (3)

4. Johan Santana – SP (4)

5. Matt Holliday – OF (8)

6. Hanley Ramirez – SS (7)

7. Brandon Webb – SP (6)

8. Josh Beckett – SP (5)

9. Magglio Ordonez – OF (11)

10. John Lackey – SP (9)

11. Jimmy Rollins – SS (14)

12. David Wright – 3B (15)

13. Erik Bedard – SP (13)

14. J.J. Putz – RP (10)

15. Aaron Harang – SP (12)

16. Dan Haren – SP (16)

17. Fausto Carmona – SP (19)

18. John Smoltz – SP (18)

19. David Ortiz – 1B (22)

20. Javier Vazquez – SP (17)

22. Cole Hamels – SP (20)

To understand the impact of each category on these players’ total points, we looked at the mean and median points per category for the 13 starters and 6 hitters (excluding Putz).

Category: Mean; Median

R: 4.4; 3.1

HR: 5.1; 3.1

RBI: 4.9; 2.0

SB: 3.5; 0.4

AVG: 5.3; 2.4

W: 4.4; 4.9

SV: -0.3; -0.3

ERA: 4.8; 5.4

WHIP: 4.4; 4.8

SO: 4.4; 4.8

While the means per category looks very consistent across the hitting vs. pitcher stats (aside from Saves), the medians per category are smaller for hitter stats. This is because even the greatest hitters are rarely great in any more than 2-3 categories. While the numbers may average out high, it’s because some players dominate the category (HR=ARod) and others are merely very good or good. A-Rod led the majors in R, HR and RBI but was outside the top 20 in Average and SB. Hanley Ramirez was top 10 in R, SB and AVG but outside the top 20 in HR and top 50 in RBI. David Wright’s isn’t in the top 10 for any category – his true value is being very good across the board. David Ortiz wouldn’t steal a base if you stuffed it with pork and deep-fried it.

Looking at the top 20 in batting average, only 5 of these players were in the top 20 for HRs (Holliday, Ortiz, Pujols, Wright, and M. Cabrera). (Note: Braun didn’t have enough ABs to qualify for average but was in top 20 for HRs)

The best pitchers, on the other hand, tend to be great or at least very good in all four categories. Looking at the top 20 in the MLB for Wins, Strikeouts, and ERA, there are 19 pitchers who are in at least two of the categories – 5 of those pitchers (Peavy, Webb, Lackey, Sabathia, and Beckett) are in the top 20 across all three.

This leads to a rather straightforward theory – starters are more likely to populate the top 20 in a player rater because the top pitchers tend to get high points in all the categories where hitters only have a couple categories where they are great.

We tested this larger theory of “pitching stats are more connected” by doing a correlation analysis on all the hitters and starters to see how closely the performance in one stat is correlated with another.

A perfect 100% would mean that the stats are absolutely correlated – say, purchases at a store and a store’s sales tax receipts (every $1 in purchases would be x% in taxes). A -100% would mean that the stats are completely inverse – say, the amount of total salary a baseball team can afford and their likelihood of picking up Jose Lima.

Below are the results of the correlation analysis.

Analysis:

90-100%

80-89% HR/RBI, ERA/WHIP

70-79%

60-69% R/RBI, W/K

50-59% R/HR, W/ERA, K/ERA, K/WHIP

40-49% R/SB, R/AVG, W/WHIP

30-39% RBI/AVG

20-29%

10-19% HR/AVG, SB/AVG

0-9%

Negative HR/SB, RBI/SB, all stats with saves for starters

Let’s start with the highest correlating stats for hitters and pitchers: HR/RBI and ERA/WHIP. The fact that these stats correlate highest should be rather self-evident.

Those that do well in HR and RBI correlate positively with Runs but poorly with SB and AVG. There is barely any correlation on HR/AVG and a mild one for RBI/AVG (which makes sense since it does require hits generally to drive in runs). SBs are negatively correlated to HR and RBI – not a surprise to anyone who has ever drafted Juan Pierre, Scott Podsednik, or Willy Taveras.

Runs prove to be an interesting category as, besides HR/RBI, they also correlate well with SBs and AVG. This is likely due to high SB and AVG players being on base a lot, at the top of the lineup, and getting driven in by the HR/RBI guys.

So what we tend to have are two types of valuable hitters: Power/middle of the lineup guys who provide strong R/HR/RBI or Speed/top of the lineup guys who provide strong R/SB/AVG. Players like Magglio Ordonez hitting .360 while providing solid power numbers or Hanley Ramirez providing 29 home runs while providing great R/SB/AVG numbers are the EXCEPTIONS and not the rule.

With ERA/WHIP, these stats positively correlate at 40-60% with Wins and K’s. While there are those that do well in just one of these categories (say Wang in Wins or Kazmir in K’s, alliteration unintentional), a great starter doing well in most, if not all the categories, is more common than with hitters. Since there are more successful 3-4 category pitchers vs hitters (where you generally have to tradeoff strengths with weaknesses like Ichiro’s R/SB/AVG vs. HR/RBI), it makes sense that starters are disproportionately valuable.

The final point here – which was covered in the Peavy vs. A-Rod comparison – is the fact that starting pitchers have more influence over a team’s total stats than a hitter. This is particularly true in ERA and WHIP where a top starter may represent around 20% of your innings. Compare this to a hitter who is lucky to represent 8-9% of your ABs.

An illustrative comparison is looking at the stats of the 20th player in our rater – Javier Vazquez. His stat line of 15W/3.74/1.14/213K looks pretty good but what would the equivalent be in value for a hitter? If we link up Runs to Wins, HR to ERA, RBI to K’s, SBs to SV, and AVG to WHIP, our model would require a 120/21/137/0/.345 hitter. (fyi, if you want to see how SBs would play a role, switching the SB and AVG values would net .278 and 36 SBs)

So the answer to Question 2A is yes. We do feel that starters for any particular season should represent a majority of the top 20 value slots – unless, of course, a breed of power/speed guys start cropping up that rack up RBIs and don’t suck at AVG (see Mike Cameron, Chris B. Young).

On to Question #2B, is ESPN’s ranking of starting pitchers correct?

Let’s take a look at the pitchers just outside the top 20 in ESPN’s Player Rater and compare them to our totals.

ESPN Rank – Player Name – Our Rank

20 – C. Hamels – 22

21 – J. Verlander – 26

23 – K. Escobar – 36

24 – T. Lilly – 34

27 – J. Shields – 43

28 – T. Hudson – 42

29 – S. Kazmir – 55

Scott Kazmir’s stat line of 13W/3.48/1.38/239Ks netted him 3.25/3.58/2.55/4.98 in ESPN Player Rater points for a total of 14.36. Turning that into percentages, we’re looking at 22% for Wins, 25% for ERA, 18% for WHIP, and 35% for K’s.

In our rater, Scott Kazmir had 10.5 points that netted him 2.2/3.2/-1.4/6.8 or 21% for Wins, 31% for ERA, -14% for WHIP, and 64.5% for K’s.

Why the negative in WHIP? Because the Best Available Option (BAO) pitcher had a WHIP of 1.32 which bests Kazmir’s 1.38. His WHIP hurts your team, but you’ll take it because he does well in the other stats – especially K’s where he’s truly excellent.

The Kazmir comparison highlights several flaws in ESPN’s ranking of pitchers:

1) Capping High Points at 5 – Kazmir’s contribution in strikeouts has a greater impact on a team than, say, A-Rod’s run total. Treating them both at around 5 distorts Kazmir’s one special thing. It’s as if he’s Dirk Diggler and his “one special thing” has been shortened a couple of inches. (We’ll explore this concept further in another post – the capping, not fictional schlongs.)

2) Positive ERA/WHIP Contributions Are Undercredited – Kazmir’s 206.2 IP at a 3.48 ERA warrants slightly more credit than ESPN doles out since it is well below the BAO ERA of 3.96. If you estimate Kazmir represents between 1/6 and 1/7 of a team’s innings (figure 4 more starters = 800 IP, 4 relievers = 300 IP, total of 1300 IP which is about 1/6.5 of Kazmir’s total), that 0.48 difference in ERA nets out to about a 0.07-0.08 drop in team ERA. This is equivalent to the impact of a player hitting about .337 or driving in 116 RBIs. (This concept is also covered in the A-Rod vs. Peavy post.)

3) Negative Contributions Aren’t Penalized – Kazmir’s WHIP of 1.38 is below the 1.32 WHIP of the Best Available Option pitcher (the top FA starter if the best 50 starters and 40 relievers were taken). How could this net positive points? He’s hurting your team. The WHIP is a tradeoff for his other stats. It’s like going out with a girl because she’s hot AND crazy instead of because she’s hot and IN SPITE of the fact she’s crazy. This ‘tradeoff’ cost is also present in counting stats for second-tier pitchers like C. Wang (gets less than average strikeouts) or Chris “Tall San Diego Pitcher” Young (his 9 wins are less than average).

A 4th issue related to #3 – the overcrediting of slightly above average performance – is more apparent with Ted Lilly’s ERA (3.83 vs BAO 3.96) and WHIP (1.14 vs. BAO 1.32). While Lilly’s ERA should get some positive credit, it is not worth nearly as much as his WHIP. ESPN’s system doesn’t value the two that differently – Lilly with 3.12 points in ERA and 4.30 in WHIP. On the other hand, we credit Lilly with 1.3 ERA points and 4.8 WHIP points.

The summary of all these ESPN Player Rater flaws is the following:

Wins – Slightly undercredits great performance (issue #1), Doesn’t penalize below average performance (issue #3)

ERA – Undercredits great ERAs, overcredits slightly above average to bad ERAs

WHIP – Undercredits great WHIPs, overcredits slightly above average to bad WHIPs

Strikeouts – Similar to Wins but more pronounced.

For the pitchers in the top 20, these effects are minor – Harang and Vazquez are slightly inflated given their so-so ERAs, Carmona is deflated given his great ERA (3.06), Beckett and Webb switch places because Webb’s ERA superiority trumps Beckett’s 2 extra wins. Outside the top 20, the results become more pronounced since issues #3 and #4 play a greater role in the distorted value.

Thus the verdict for Question #2B – is ESPN’s ranking of starting pitchers correct? The answer is no. Their rater does a fair job at the top, but it gets continually distorted as you move outside the top 20 players because it doesn’t properly penalize mediocre to below average performance.

(Note: Unfortunately, the ESPN Player Rater’s improper penalizing of below average performance has a lot in common with the internal review of Baseball Tonight anchors – please tell me John Kruk, Orestes Destrade and Eric Young aren’t coming back…)

Come March, when you’re preparing for your draft and trying to decide between pitchers, you can avoid the above mistakes by just comparing the two pitchers’ projected stats and credit a point for each of the following increments: 1.5 Wins = 0.19 ERA = 0.04 WHIP = 18 Ks. Might take a little more calculating but it could be the difference between taking Jeremy Bonderman over John Lackey (that decision still haunts me from last year…)

1) Did the ESPN employees responsible for the database crash that screwed up the first two weeks’ worth of 2007 stats befall a fate worse than Harold “Harass is one word?” Reynolds (rarely insightful on Baseball Tonight but he’s like Peter F*in’ Gammons compared to replacement ex-2B Eric Young)?

2) Is the ESPN Player Rater completely incorrect or just mostly incorrect?

While we wait to see if Outside the Lines’ Bob Ley or the ESPN Ombudslady answer our repeated requests for answers on question #1, we took on the challenge of question #2.

Now you may ask, “Is this a valuable exercise beyond the joy in potentially proving ESPN wrong?” Fair question. The answer is a resounding yes. One of the greatest challenges with Fantasy Baseball is determining how to compare the value of players and their statistical contributions – any idiot could tell you that A-Rod and Peavy were the best hitter and pitcher respectively but was Matt Holliday more valuable than Jimmy Rollins? Understanding the value of each of the statistics – ESPECIALLY in non-counting stats like batting average, ERA, and WHIP – helps from the draft throughout the season as rosters are juggled, free agent options are considered, and trade offers are mulled. (That being said, proving ESPN wrong was a motivating factor.)

In no particular order, here are some of the questions we have on the 2007 season that we don’t believe ESPN Player Rater correctly answers (based on 5×5 MLB Universe):

1) Who is more valuable: A-Rod (best hitter) vs. Peavy (best player)?

2) How could there be so many starting pitchers at the top? (13 in top 20, 19 in top 30) Is that valid or just faulty weighting?

3) ESPN creates a floor and ceiling of 0 (floor) and 5 (ceiling) for points per category. Does this misrepresent the contribution (or lack thereof) of players and what is the impact on player rankings? For example, how is Jose Reyes’ amazin’ 78 SBs worth the same amount of points as Josh Beckett’s merely impressive 20 wins? How is Richie Sexson’s horrific .205 batting average worth the same amount as Todd Helton’s sloth-like 0 SBs?

4) How is it possible that relievers J.J. Putz and Rafael Betancourt are worth more for ERA and WHIP than top starters like C.C. Sabathia, Johan Santana, and Brandon Webb when these relievers pitch about 1/3 of the innings as the starters?

5) Shouldn’t players at shallower positions receive bonus points – e.g, are Hanley Ramirez’s stats at SS more valuable than A-Rod’s stats for 3B?

To tackle these questions, we created our own Player Rater. See attached for the rankings and notes on the methodology we used. The methodology is nerdy and quasi-scientific – with heavy conceptual influence from Baseball Prospectus, Bill James, and other leaders in the field – but you could skip all the mathy stuff and just look at the rankings if you like.

We will answer these questions – and potentially others – over several posts. Let’s start off with the first one…

1) Who is more valuable: A-Rod (best hitter) vs. Peavy (best pitcher)?

ESPN has A-Rod #1 and Jake Peavy #2 by the slimmest of margins – 19.8 to 19.75. This order is in line with popular opinion. I’m sure if you were to do a ‘hindsight draft’ – whereby you draft based on 2007 stats – that Scott Boras’s wet dream would be picked #1 almost every time. There’s a much greater chance Peavy would go #3 or later than #1.

But is this some type of hitter/East Coast bias vs. pitcher/West Coast bias? Well, let’s look at how ESPN calculated the point totals:

A-Rod: Runs = 5, HR = 5, RBI = 5, SB=1.54, AVG=3.26
Peavy: K = 5, W = 4.75, SV = 0, ERA = 5, WHIP = 5

A-Rod led the majors in R/HR/RBI to earn the three category max of 15 points. His above average 24 SBs and .314 AVG netted him another 4.8 points. Peavy had a possible max of 20 points since Bud Black inexplicably turned to Trevor Hoffman to close games and was a win short of getting all 20 (should’ve showed up in that one-game playoff, Jake). So, maybe that’s it. Even the best starting pitchers can only contribute in 4 categories so a great 5-category hitter is more valuable.

What do our rankings say? It says Mr. Peavy is #1 by a healthy margin – 29.4 to 26.2 points. I’ll admit it – we were surprised too. Let’s look at our point allocations by category to explain it:

A-Rod: Runs = 4.9, HR = 8.3, RBI = 6.8, SB=3.2, AVG=3.0
Peavy: K = 6.8, W = 6.2, SV = -0.3, ERA = 9.3, WHIP = 7.4

This comparison is as good an opportunity as any to go over our methodological basis for crediting points (skip over if you’re mathphobic):

Rather than using a 0-5 point scale per category, we mirrored the VORP concept from Baseball Prospectus and created composite stats for what would be the best available option (BAO) at each position – e.g., what are the stats of the 11th best catcher who’d be the next best option post-draft. We then created a team full of these BAOs (kind of like a fantasy expansion team – hell, a team with Carlos Pena, BJ Upton, Troy Tulowitzki, Ryan Braun is much better than the MLB variety) and averaged their stats to create the BAO hitter and BAO pitcher.

We then took the team totals of our ESPN league to come up with relevant increments to award points. Our method would credit players with positive points if they performed above the BAO in a stat and negative points if they performed below (example: Magglio Ordonez was worth 7.7 AVG points while Richie Sexson was worth -4.6 points)

The increments were based on the standard deviation between our 10 teams’ totals which came out to roughly 1-2% of the average team’s total for R, HR, RBI, K, and W. SB and SV turned out around 4% of the average because team totals in these stats tend to be more widely distributed than the other counting stats (related to few players contributing the lion’s share of points – another way of explaining this is to consider the impact of 5 Wins vs. SBs and SVs on your league’s rankings. Wins would prove more valuable.). Lastly, the ratio stats – AVG, ERA, and WHIP – are around 0.5% of the average team’s total as there is much smaller % change between players (e.g., a great hitter hits .350, a bad hitter .250. That’s only a 40% difference. A-Rod hit 150% more HRs than fellow MVP candidate Mike Lowell)

These standard deviations were arbitrarily divided by 6 to create more point differential between players. For ratios, the team totals were multiplied up to reflect an individual player’s impact on the total – so a hitter would have to hit .0144 better than the BAO (assuming all 13 hitters had the same # of ABs) to raise the team’s average by the required .0011.

Lastly, we compared each player to two different types of BAOs: one specific to their position and one general (hitter or pitcher). These results were averaged together and helped to account for the fact that a BAO 1st baseman offers better stats than a BAO catcher so, all stats equal, the catcher is a more valuable hitter. (This topic will be further explored in another post. We’ve only got so much material to stretch over the offseason.)

So now let’s look at Runs vs. K’s to see this methodology in action. These make a good comparison in that the average team totals for these stats (based on our ESPN league) are nearly equal: 1150 runs and 1148 Ks.

A-Rod and Peavy led the majors in these categories (143 Rs and 240 Ks respectively) so ESPN credits each with 5 points. But the BAO hitter (who looks almost exactly like Luis Gonzalez’s stat line of .278/70/15/68/6) had 67 runs where the BAO pitcher (who looks closest to Carlos Villanueva’s season of 8 Ws/1 SV/3.94/1.35/99 K over 114 IP) had 101 K’s.

(Note: That may seem low for K’s but here’s a few other starting pitchers b/w 90 and 110 K’s that certainly saw some fantasy roster space during the season: C. Wang, G. Maddux, B. Sheets, C. Schilling, M. Mussina, J. Marquis)

So A-Rod had 76 more Runs than the BAO hitter and Peavy had 139 K’s more than the BAO in a category where teams had virtually the same average total. Another way of looking at it is that Peavy’s total would represent 20.9% of the average team’s total where A-Rod’s runs would be 12.4%.

The larger differential and impact of Peavy’s K’s vs. A-Rod’s runs are slightly curbed by the fact that Runs have a lower standard deviation which leads to crediting a point for 15.1 runs and 18.1 K’s. This nets out to 4.9 points for A-Rod’s Runs vs. 6.8 for Peavy’s K’s.

Peavy’s impact on Wins is similar to his impact in K’s and A-Rod’s HR and RBI totals net him 8.3 and 6.8 points respectively. His 24 SBs net him 3.2 points. (If anything, the ESPN total screws him – how could 24 SBs be worth only 1.5 points of 5 points, with 0 SBs equaling 0 points, given the average team only had 162 SBs?)

So it all comes down to the ratio stats (AVG, ERA, WHIP) to determine the winner.

Let’s look at A-Rod’s AVG first. To determine the impact, we start with a lineup made exclusively of BAO hitters. This group hits .2772. If we replace one BAO with A-Rod, the average goes up to .2806 – a difference of 0.0034. In addition, A-Rod had 583 ABs compared to our BAO’s 482 ABs. So A-Rod’s average has a greater impact than just 1/13th (it becomes worth about 1/11th). To account for this, we multiply his points by his # of ABs / BAO ABs (1.2). After accounting for the fact that the 3B BAO hits for a slightly higher average (.279), A-Rod’s batting average nets him 2.8 points.

Now let’s look at Peavy’s MLB-leading 2.54 ERA and 1.06 WHIP. Adding him to a BAO team (5 starters + 4 relievers with removing a BAO composite of the two) changes the team ERA/WHIP from 3.960/1.315 to 3.719/1.272. This is a net decrease of 0.241 in ERA and 0.043 in WHIP.

How huge is this? Well, looking at our league, this would be the number of points gained in ERA by deducting .241 (1 team – 5 pts, 2 teams – 4 pts, 2 teams – 3 pts, 3 teams – 2 pts, 1 team – 1 pt, 1 team – 0 pts). If you factor in that the 1st, 2nd, and 3rd place teams had a maximum of 0, 1 , and 2 points to gain respectively, you can see that at a MINIMUM, Jake Peavy’s ERA would’ve gained a team 2 points and more likely 3+. The WHIP difference is similar in impact.

Now we compared Peavy against our BAO pitcher who is a composite of the best pitcher available. This could be a starter or reliever. What if we compared him against strictly the BAO starter (the 51st best starter)? This would reduce the impact of Peavy’s Wins and K’s but would actually INCREASE the impact of his ERA and WHIP. Why? Because starters pitch more innings than relievers and tend to give up more runs and baserunners. The BAO starter has an ERA of 4.15 and WHIP of 1.34 (closest comparison – Carlos Silva) where the BAO reliever is at an ERA of 3.31 and WHIP of 1.24. Compared to the BAO Starter, his ERA is worth 9.9 ‘position points’ but 8.6 ‘player points’ for an average of 9.3 points.

In summary, we would say Jake Peavy was the MVP of 2007 Fantasy Baseball (5×5, MLB Universe) over A-Rod. While A-Rod had two high contributing categories (HR and RBI), one strong (Runs), and two above average (SB and AVG), Peavy has four high contributing categories with his ERA and WHIP point total of 16.7 dwarfing his closest competitors (Santana = 11.3, Sabathia = 11.2).

While we wouldn’t have the nut sack to draft Peavy #1 in a 2008 draft (pitching is less predictable than hitting), he’d be our choice for #1 in a ‘hindsight’ draft. And if you can’t even manage 20/20 hindsight, how can you expect to see clearly into the future…