Fantasy Baseball Advice

With great pride and bland post titling, I’d like to announce a Beta release of our fantasy baseball in-season player rater as well as two charts that highlight the differences between pitcher FIP vs. ERA and batter BABIP vs. AVG.

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.

One of the keys to a successful fantasy season is not drafting a pitcher who misses an extensive amount of time or performs much worse than the previous year.  Anyone who drafted Chris Carpenter or Dontrelle Willis in 2007 or Rich Hill or Aaron Harang in 2008 can attest to this.

There have been several theories posed in traditional publications (SI.com – Tom Verducci) and in the fantasy baseball blogosphere (FantasyPhenoms, Beyond the Box Score, RotoAuthority) that claim a correlation between a pitcher’s innings/pitches from the previous year and their performance the next year.

While these articles mentioned several positive examples, none of them really tested their theories to see if they are true predictors of either injury or decreased performance.  We decided to put them to the test along with several theories we’ve been kicking around:

• High Pitch Volume – Does a high volume of pitches have a carryover effect the next year?  (posed by FantasyPhenoms and RotoAuthority)
• Spike In Pitch Volume - What is the effect of a significant increase in pitch volume vs. previous year?  (posed by Tom Verducci)
• New To The Workload - Is a pitcher who first reaches the 2,700 pitch threshold more likely to fall back the next year vs. a pitcher used to the workload?
• High % of Breaking Pitches – Is a pitcher who throws breaking balls more susceptible to fall back vs. a fastball/changeup pitcher?

We also added a Common Sense test which predicted that weaker pitchers from year prior (4.00+ FIP) would be more likely to get hurt, moved to the bullpen, or demoted to the minors than stronger pitchers.  This ‘common sense’ test also serves as a proxy for a typical fantasy baseball drafter’s judgment as they would likely have more faith in pitchers who were successful in the previous year.

For the test, we focused on the past three years (2006-2008) and on pitchers who threw at least 2,700 pitches (~ 155-160 IP) in that year.  Pitchers who retired/semi-retired the next season for reasons other than injury were removed.  This left a total of 247 pitcher seasons.  We defined ‘miss significant time’ as someone who threw 2,000 or less pitches (~ 20 GS) in MLB the next year and ‘measurable decrease in performance’ as a 0.50 or more increase in their FIP (this is an ERA variation that stands for Fielding Independent Pitching based on only things within a pitcher’s control – K, BB, HR – and is more stable year-to-year than ERA).  If a pitcher threw less than 2,000 pitches the next year, we ignored their FIP so as not to add insult to injury (ha!).

It’s worth noting that throwing 2,000 or less pitches in MLB does not necessarily mean someone was injured – it could also mean their performance decreased to a level that was no longer MLB-worthy and they were demoted.  Since these amount to the same thing for a fantasy team owner (bupkus), we did not differentiate.  We also did not credit for pitches thrown in the minor leagues prior to arriving in the MLB or pitches thrown in the postseason.  This is partly out of convenience (our data source FanGraphs does not include this in their ‘Leaders’ section) and partly out of intent (a pitch in the minor leagues is not as stressful as a pitch in the major leagues).

In total, 59 (24%) of the 247 qualified pitcher seasons were followed up by seasons of less than 2,000 pitches and 53 (21%) were followed up with FIP increases above 0.50.  That means that about 45% of starting pitchers either pitched significantly less or had a measurable decrease in performance in the following year.  Ouch!

Below are the test results:

The best theory proved to be the High % of Breaking Pitches Theory as both the 27+% and 30+% thresholds finished in the top two for index (137 and 128 respectively – the 137 means that a pitcher who threw 30+% Sliders/Curve Balls in the previous year is 37% more likely than the average pitcher to fall below 2,000 pitchers or see their FIP increase 0.50+.  The Spike in Pitch Volume Theory (go Verducci!) and New to the Workload Theory also performed above average.

The ‘Common Sense’ test (FIP 4.00+) finished  at about a 100 index meaning picking a random pitcher would’ve been as effective as choosing one with this criteria.  This average performance may be misleading since it did a better than average job at predicting significant pitch decreases (29% to 24%) and its below average performance at predicting FIP increases may be because some of the FIPs were so high to begin with in the first place.

The High Pitch Volume Theory proved to be a relative failure.  We tried four variations – one-year (3400+ and 3500+), two year (6500+) and three year (9000+) and all of them finished at a below average rate.  Both the 3400+ and 3500+ theories did a good job at predicting 0.50+ FIP increases but that is tempered by the fact that they had a higher percentage of pitchers throw enough innings to qualify.  For instance, if you were to back out the 19 of 56 pitchers who fell below 2000 pitches in the Spike In Pitch Volume Theory, you would have 38% (14 of 37) that had 0.50+ FIP increases.  So there may be a slight correlation that a heavily used pitcher does not perform as effectively the next year but they will perform.

I’d theorize the failure of High Pitch Volume Theory in predicting significant pitch declines is because totaling high pitch counts in a year is more of a skill than an abuse.  It is easier for someone who has run a marathon in the past to run another one versus someone who has not.  While this group is not immune to missing time, they have built the stamina to at least pitch close to a full season (2700+ pitches).  Below are the 16 members of 2007′s “3400+ Pitch Club” .

The collective average went from 3,534 pitches to 2,991 pitches which, though a decline,  still represents about 29 starts.  8 of the 16 pitchers on this list finished in the top 30 in 2008 total pitches.  The below 100 index in the test and the above chart indicate that a pitcher with 3400+ pitches in the previous year is a sign of durability not vulnerability (note: given the high injury rate with pitchers, though, it’s not that strong of a sign).

We then tested several combinations of the three above-average performing theories and threw a last bone to the High Pitch Volume theory (which tanked even after adding the High % of Breaking Balls requirement).

While the top two combo theories are very good predictors, they are also very rare – about 1 in 25 pitching seasons.  The most impressive predictor of the bunch is test #6 – 27+% Sliders/Curve Balls OR 700+ Pitch Spike.   Why?  Because that 128 index applied to a huge sample – nearly half of the pitcher seasons in the study.  The impact is best seen by comparing this group against its mirror image – pitchers who threw < 27% Sliders/Curve Balls AND had < 700+ Pitch Spike.

So a pitcher that qualifies in at least one of these categories is 28% more likely to throw less than 2000 pitches in the next year or to see an FIP increase of 0.50+ than the average pitcher.  Just as important, one that qualifies for neither category is 24% less likely than the average pitcher to meet these respective fates.

That’s it for now but we’ll be revisiting this topic in the near future.  In the next post, we’ll go over the top 20 starting pitchers risks for 2008, perhaps uncovering a couple pitchers that you’ll be surprised to see…