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Fantasy baseballers are becoming increasingly analytical. Estimating a player’s future batting average now reflexively leads to checking their BABIP, their batted ball profile (GB/LD/FB) and a hitter’s K%. Any discussion of a pitcher’s ERA will likely reference their FIP/xFIP/SIERA/etc. Aside from the whole bastardization of baseball outcomes for the illusion of empowerment and erosion of professional productivity, the average fantasy baseballer is much closer in perspective to a sabermetrician than the average fan watching from his/her couch (or the remaining indoctrinated baseball journalists that still roam the land).

One area where I admit I have a tough time reconciling my analytic side with my fantasy baseball instincts is the value of a hitter’s recent performance.*

* I know many fantasy baseball players look at hitter/pitcher matchup data. I think the chapter on this in The Book clearly drives home the point that these results are not predictive because of small sample sizes. I always ignore matchup data and think it is a total waste of time…well, except for Goldschmidt vs Lincecum

It seems perfectly natural to gravitate towards hitters with recent success (say, multiple hits in the past couple games, 2 HR in past 3 games, etc) versus someone who has thrown up a couple of 0-fers. But could 10-20 PA produce any semblance of a statistical signal with all that small sample noise (e.g., even if there are cases where a player is truly ‘hot’ and outperforming his skill level because of some combination of health/confidence/mojo, small samples also bring cases where a player just flukes into a couple 2-hit games or takes advantage of a couple of hanging sliders).

As a tie-breaker between two equally projected hitters, recent performance is, at worst, a harmless impulse.  The harder question to answer is, “At what point should you choose a hitter with weaker projections because they hit better in the past couple of games?” Given I manage our daily hitter projections (Hittertron and the hitter portion of DFSBot) – which adjust Steamer Rest of Season projections with various gameday adjustments like opposing pitcher and park factors – the key question rattling in my brain is:

Is recent game performance properly accounted for in our Hittertron/DFSBot daily hitter projections?

Our daily projections incorporate two key elements:

  1. Estimated Talent/Skill from Rest of Season Steamer Projections which weighs in-season performance against previous year performance and is updated daily.
  2. Game-Specific context such as Opposing pitcher, Park factors, Home/Away, likelihood to start, and expected spot in lineup.

If ‘hot streaks’ and ‘cold streaks’ influence a player’s next game performance (and, thus, a player performs above/below their general talent level), that would represent a third element. I believe most fantasy baseball players THINK like this but is it correct thinking?

If you google ‘hitter streakiness‘, you will find some wonky-ass analyses. Probably all more brilliant than I could have thought up. But they do not answer my question directly nor will they likely make much sense to anyone reading this rag of a blog.

So I did my own analysis – one fashioned in a way that should make sense to Razzballers AND help improve the Hittertron/DFSBot should there be predictive value in recent game performance.

Here is the overview of the analysis (note:  I have done the same for starting pitchers vs Streamonator and will release in a future post) :

  • Create two hitter data sets based on 2014 data:  1) Every hitter that started 4 straight games (for a ‘last 3 game’ vs ‘next game’ analysis) and 2) at least 5 of his last 6 games (for a ‘last 5 game’ vs ‘next game’ analysis)
    • Note:  I ended up limiting this to May-August 2014 and players with 500+ PA in 2014 due to data size challenges
  • Compile the hitter’s stats for the ‘last 3 games’ and ‘last 5 games’ (if they started 4 of last 5 games, only count the games they started and divide by 4)
  • Compile the Hittertron projections for the ‘last 3 games’ and ‘last 5 games’ (if they started 4 of last 5 games, only add the projections for the games they started)
  • Determine the correlations between a player’s ‘next game’ stats versus:  1) their Hittertron projections for that game, 2) their  ‘last 3 game’ and ‘last 5 game’ stats, and 3) the difference between their Hittertron ‘last 3 game’ / ‘last 5 game’ projections and their actual ‘last 3 game’ / ‘last 5 game’ stats.
    • Re: #3, this helps separate ‘streakiness’ from ‘skill`.  If Jose Altuve hits 2 HRs in last 3 games, that is more surprising/meaningful than if Giancarlo Stanton does.
  • Determine the correlation using a regression based on Hittertron AND the ‘last 3 game’ / ‘last 5 game’ stats to see if recent game performance provides additional predictive value.

A huge advantage of leveraging the Hittertron projections as part of this analysis is that it neutralizes the ‘context’ variables when comparing previous game performance.  For example, Hittertron factors in the ballpark and home/away status.  If a Rockie played his three previous games in Colorado and is now on the road at Petco, it is very likely he will hit worse.  That could look like a false dip in performance when it is comparable performance once you subtract the higher projected stats expected at Coors vs Petco. The same goes for opposing pitcher strength and facing runs of RHP or LHP.

Here are the results of the analysis:

Game #4 Correlations Of Daily Stat Projections For Batters Who Started Previous 3 Team Games (149 unique batters, 7,169 instances)
Stat Hittertron Prev 3 Games Minus Hittertron Proj Previous 3 Games Hittertron, Prev 3 Games Minus Proj (regression on both variables) Improvement (Column 4 minus Column 1)
HR 15.2% 0.4% 4.7% 15.2% 0.0%
SB 23.2% 3.2% 12.6% 23.4% 0.2%
AVG 9.4% 1.9% 2.8% 9.6% 0.2%
OBP 11.1% 1.5% 2.8% 11.2% 0.1%
R 10.8% -1.6% 0.1% N/A N/A
RBI 13.7% 1.0% 3.2% 13.7% 0.0%

 

Game #6 Correlations Of Daily Stat Projections For Batters Who Started At Least 4 of Previous 5 Team Games (149 unique batters, 10,236 instances)
Stat Hittertron Prev 5 Games Minus Hittertron Proj Previous 5 Games Hittertron, Prev 5 G Avg Minus Proj (regression on both variables) Improvement
HR 15.4% 2.2% 6.5% 15.6% 0.2%
SB 24.8% (0.8%) 11.2% 24.8% 0.0%
AVG 9.5% 1.3% 2.5% 9.6% 0.1%
OBP 10.6% 0.8% 2.6% 10.6% 0.0%
R 11.0% (-0.7%) 1.9% N/A N/A
RBI 12.8% 1.3% 2.6% 13.0% 0.2%

The analysis shows that previous 3 and 5 day performance provides zero to negligible (and likely not statistically significant) improvement upon the Hittertron results. The previous game averages (4th columns) have minor correlations with ‘next game’ results but most of that is a reflection of player skills that are already accounted for in the Hittertron results (hence, the correlations in the 3rd column are virtually all 0%).

Still not convinced?  The below tables show the actual vs projected performance for hitters based on how many stolen bases and home runs each player had in the past 3 games. If streakiness had statistical significance, players who had multiple HR and SB should outperform their projections compared to those with zero HR/SB. But that is not the case.  While there is a positive correlation between last 3 game SBs and next game SB, the fact that the projections skew in the same way indicates this is a reflection of general skill level and not a short-term burst. (Note: The reason why HR and SB projections are higher than actual in all cases is that 2014 was a lower offensive environment than expected.  The inverse should be true for pitchers in my next analysis – that the actual results overperformed the projections)

Game #4 Stolen Bases For Batters Who Started Previous 3 Team Games (149 unique batters, 7,169 instances)
Last 3 Game SBs # of Instances Next Game Avg SB (Actual) Next Game Avg SB (Projected) Next G Avg SB Per 162 Games (Actual) Next G Avg SB Per 162 Games (Projected)
0 5852 0.06 0.07 9.4 10.9
1 1021 0.12 0.13 19.0 20.5
2 229 0.18 0.18 29.0 29.5
3 45 0.20 0.25 32.4 40.5
4 13 0.31 0.33 49.8 54.0
5 7 0.29 0.29 46.3 47.0
6 2 1.50 0.25 243.0 40.5

 

Game #4 Home Runs For Batters Who Started Previous 3 Team Games (149 unique batters, 7,169 instances)
Last 3 Game SBs # of Instances Next Game Avg HR (Actual) Next Game Avg HR (Projected) Next G Avg HR Per 162 Games (Actual) Next G Avg HR Per 162 Games (Projected)
0 5024 0.10 0.12 16.5 19.2
1 1778 0.13 0.15 20.3 24.2
2 322 0.17 0.17 27.2 27.9
3 41 0.17 0.20 27.7 32.1
4 4 0.00 0.24 0.0 38.5

I am a little surprised that recent SBs provide no additional benefit given that SB attempts are driven at least somewhat by confidence (so a number of successful attempts would embolden the manager or player) and that slight changes in a player’s health throughout the year would lead to stretches where a speedster will not attempt SBs. I do think that SBs, like male elephants, come in bunches but a lot of this is influenced by game-by-game variables like the pitcher, the catcher, and score (close game, blowout, etc) so previous game SBs are just statistical noise.

Conclusions

There is nothing to gain from adding recent game performance into the hitter projection mix and one should not choose a lesser-projected player over another because they have done well in previous games. Assuming equal Hittertron (or DFSBot) projections and both hitters are starting that next day, choosing the ‘hotter’ player is perfectly fine. As is choosing the hitter who has performed better against the opposing pitcher. Or choosing the hitter whose last name comes first alphabetically. Or choosing the hitter who is on your favorite team. Because each of these approaches will net out to about the same results in the long run.

Is it possible that recent 3-5 game performance helps improve other daily projection systems? Possibly, but IMHO this would be a flashing neon warning sign that said projection system is inferior to Hittertron/DFSBot. Based on this analysis, a projection system that learns something new from a hitter’s recent 3-5 game performance is as comforting as hearing your doctor say, “Hmm, this Wikipedia article on <fill in malady> has got me thinking…”.

Is it possible that there is some interval of games greater than ‘last 5’ that provide additional predictive value to Hittertron? I do not think so. Certainly greater in-season intervals (e.g., last 30 games) are better predictors than smaller in-season intervals but I think the increases sample improves ‘skill/talent’ measurement that is already properly weighted by the Steamer projections (e.g., AVG does not have predictive power until ~900 AB).

One last thought for Daily Fantasy players. Recent game performance may well be a market inequity that one could exploit – particularly in Daily Fantasy games.  If DFS participants’ bias towards ‘hot’ hitters (and DFS $ values may reflect recent game performance), it may pay to target ‘cold’ players as they may be more affordable and produce more unique lineups.

  1. Jake says:
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    Hi Rudy,

    Tech question: Are you aware of excel functions that I could use to pull data from the stream-o-nator and hit-o-tron? and/or possibly espn?

    If not any tips on scraping using visual basic to accomplish this?

    I have limited experience, but it’s too manual of a process to copy and paste every day (or real time as rosters are set). Any direction would be appreciated.

    Thanks!

    • There is one way but not sure how well it plays with our log-in process. In Excel top menu, choose Data and then From Web. Put the page URL in. Click on the arrow in the page. Done. Then just need to refresh next time u go in.

      • Jake says:
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        @Rudy Gamble: Cool. Great piece. I guess this means it’s not worthwhile to chase Grey’s hot schmotatos?

        • It may be – if they synch up with hittertron

      • Farley says:
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        @Rudy Gamble: Tried this and it does not play well with the log-in. I can log-in myself on the page but when it pulls the data it just comes up as “This article is paid content! To access, you can pay Fantasy Baseball Blog at Razzball.com with CoinTent. To use CoinTent, you must enable javascript.” As I am not the most well versed in Excel, there may be a way to make it work that I am unaware of. Any thoughts?

        • Probably not. Hadn’t tried it myself but this seems like a logical outcome. Cut/paste it is….

  2. Sky

    Sky says:
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    That asterisk footnote requires a telescope…but you made up for it using ‘wonky-ass’.

    Of course, none of that matters. This is some damn fine analysis, Rudy. Retweet forthcoming.

  3. Fred says:
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    Hey Rudy man
    My 2b is odor , would you bench odor for week 1 ; im in a h2h points weekly lock league here are available second baseman:
    Howie kendrick, neil Walker, Brett lawrie (eligible at 2b & 3b), arismendy alcantara , Jedd gyorko, jace peterson (eligible at SS & 2b), Micah johnson, marcus semien (eligible at 3b & 2b)

  4. Chuckles Tiddlesworth says:
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    What about all of Grey’s hot schmotatoes???

    • Always worth a shot if their hittertron values are close

  5. mrrr says:
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    Very nice work Rudy. Kudos to your Hittertron work to account for the gameday variables and get at a context neutral setting.

    • Thanks. BTW, I never told you this before but I enjoy your comments much better than those of frankincense!

      • mrrr says:
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        @Rudy Gamble: Thanks! Goldy still has me beat though.

  6. Left arm of god says:
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    In a roto 6 category avg hr on base plus slugging sb RBI and runs keep forever league would you trade Anthony Rizzo for miggy

    • No they are in a similar range now and miggy older

  7. David says:
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    I would think that recent performance might correlate to more playing time, which is a good thing, even if just because a team has an injury causing the playing time. Does recent performance correlate to more playing time?

    • I imagine it does. That is real hard to test though because how would you control for other reasons to start/sit?

  8. MattTruss

    MattTruss says:
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    Awesome Rudy. I think one of the hardest but most important things in DFS is having a short memory. It’s so tough though. Remember that period last season when Caleb Joseph homered in 5 straight games? Most people probably don’t, but if you were burned by what seemed like EVERYONE playing him by that third or fourth night, then you might. It was so silly and yet, that stuff sticks with you.

    I tend to take the approach of using these small sample size bits as extra confirmation. If the data is pointing one way AND the guy has had success against that pitcher AND he’s on a “hot streak” that just confirms my feelings on a guy. He becomes more of a “must play”. Maybe now I roster him on 90% of my rosters instead of 70%. Data first, backed up by the anecdotal evidence if there is any. I can’t help myself sometimes, just doesn’t seem right to completely ignore it.

  9. S P says:
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    Intuitively, I agree with this 99% but what about players who are known for streaks? In the greater context of all MLB players, these guys’ tendencies would get statistically suppressed.

    Also, there’s the case of a player having turned some sort of corner. Statistically, it wouldn’t show up at all because a hot streak becomes a new standard, but if you didn’t grab the hot player you missed out.

    I think, personally, there are still good reasons to pick up a hot player. Last year, Vargas’ and Khris Davis’ hot streaks won me my league in the end.

    • Event changes like player health, retooled swing, new pitch, etc, are not factored into Hittertron and can have real impact. Those are the signals I hoped to find in the data but no luck

      • ADP says:
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        @Rudy Gamble: have you considered narrowing the data to players on declining trends? My thought is that players who have been doing worse in the last 3-5 games compared to all games played that season might show deterioration in performance. This would work like a moving average chart. So when the player dips far enough below their moving average, does that develop into a trend where that player’s ROS stats averages below how the player performed in the season thus far. By doing this, I’m wondering if we could use a process of elimination, if you will, to say that there are certain players who have dipped below their moving average (say 20 games or so) that may show signs of an injury or something else that might indicate players to avoid. By using this process of elimination, I’m thinking maybe there are times when, as you point out in the article, it’s anyone’s guess; while other times, better to avoid certain players. Thoughts?

        • I didn’t isolate poor performance – aside from having 0 buckets for HR and SB. It is possible that if I isolated something like accelerate K-rates or prolonged 0-fer that there is a little something that is predictive. Something to think about…

          • ADP says:
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            @Rudy Gamble: sounds good. If you decide to isolate poor performance, I think you may want to consider first defining streaky players and eliminating them from the raw data. Streaky players would either need to be defined as 1=yes=streaky or 2=no=not streaky. Then the computation would have a better chance of being statistically significant. How you define players as streaky is the question. I guess you’d first need to be able to verify a formula for defining streakiness in order for verification of deterioration in performance to be statistically significant. I would think some combination of high K%, H%, BB%, and FB% would get us a high R-squared. Or maybe this is an exercise in futility. Although if it did work, that would be something.

            • ADP says:
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              @ADP: I partially retract this statement. A high K% FB% and low H% BB% just means a player is not good, obviously. I guess a player would need to show tendencies of going multiple consecutive games above their average performance followed or preceded by multiple consecutive games below their avg performance. Seems tough to imagine this would be predictive don’t ya think?

  10. Mostsuckass says:
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    This answers one of my biggest questions about Hittertron. Have you done any similar analysis about past performance of hitters vs. individual pitchers, and whether or not it would help to add this data to Hittertron? This is a concept that many sites promote (and of course players talk about particular pitchers/hitters they hated facing for whatever reason), and I’m wondering if there’s any statistically significant connection.

    Let’s say I’m choosing between starting Corey Dickerson and Carlos Gonzalez for a day they’ll both be facing Kershaw. Hittertron happens to give them an identical dollar value for that day. Dickerson is 0-for-6 in his career against Kersaw with 4 strikeouts. Let’s suppose (though it’s not true) that CarGo is 17-for-40 in his career against Kershaw with 6 dingers. Does the data suggest there’s any reason to go with CarGo? Or is it really a crapshoot?

    • LOL, see the smaller font in the beginning of the post.

      • Mostsuckass says:
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        @Rudy Gamble: Hey Rudy, thanks for answering my silly (I realize now) question. I hadn’t read the small print because my eyesight is truly terrible.

          • Mostsuckass says:
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            @Rudy Gamble: I wonder how often I skip reading the fine print because it’s too much trouble, and what I’d know now if I’d bothered reading it.

  11. Kap says:
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    Where do you get raw MLB data from? Is there a subscription you can buy from the league?

    • We have a daily data feed from a third- party company. Costs $$

  12. royce! says:
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    Really interesting stuff, Rudy. I read something similar recently regarding streakiness in basketball shooting (i.e., is there such thing as a hot hand) in How Not To Be Wrong by Jordan Ellenberg. I think his conclusion was that if there is such a thing as a “hot hand,” its effect is weak and brief, and that players would probably be better off not considering it because it can lead one to take ill-advised shots. I imagine the same is true about baseball- hot streaks exist, but the effect of being “hot” is brief and nearly negligible.

    • Thanks and, yes, I think that’d be the case for baseball too

  13. royce! says:
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    Also, I could use your smarts in trying to figure out something. I recently joined a league that counts the normal 5 categories plus QS and L for pitchers. The league also has a 1250 IP limit. Given the low IP limit and the additional categories, how do you think I should value high k/9 middle relievers like Wade Davis? It seems that without the IP limit, they would have the same value, but with the low IP limit, the more innings you use up with a MR, the less you have to go after QS.

    FWIW, this is my pitching staff (12 team auction):

    SP- Scherzer
    SP- Kulber
    SP- Tanaka
    SP- Quintana
    RP- Chapman
    RP- Kimbrel
    P- Papelbon
    P- A. Miller
    BN- Kennedy
    BN- Parnell
    BN- Smyly
    DL- Verlander

    • RotoLance

      RotoLance says:
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      @royce!: I’m going to chime in because I love player value questions like this (Sorry If I’m out of line here Rudy).

      The thing is, if you assume everyone is going to end up with exactly 1250 IP then every pitching stat becomes a rate stat. It creates a weird dynamic where you want your starting pitchers to be removed from their game as soon as they get their QS and likely W in. Most significantly perhaps is that the K stat is the same as if they’d actually used K/9.

      The answer to your question is actually quite complex. To simplify it I think you really need to just react to what your league-mates are doing and just play their game but play it better, which you seem to be in a good position to be able to do with your quality of pitching. But honestly I’d want to risk being mediocre in QS and W if it means being tops in ERA, WHIP and K.

      • royce! says:
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        @RotoLance: Interesting take, thanks! I just picked up Wade Davis, gonna put your advice into practice. My hope is that given the strength of my top 3 SP and possibly a streaming spot (using stream-o-nator), I can get a good number of QS and W, making MRs lack of contribution to those categories less malignant.

        • In IP cap leagues, I agree with Lance. relievers very valuable and I avoid rostering any SPs besides top 50% SPs.

  14. Switch Hitter says:
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    Fourth yr. razzball reader and I subscribed to the daily stats today. Despite playing in free league, I 1) Wanted to support one of my favorite websites 2) Couldn’t make the proper decisions as often without your bots.

    Thanks Razzball team for all that you do! (Even Tehol)

  15. Zach says:
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    @Sky @RudyGamble Trade question for you.

    Would you trade away Harper and Carrasco for Joey Bats and Uehara? I would be the team who gets Joey.

    Thanks for the input!

  16. Yescheese says:
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    Love Hittertron/Streamonator, didn’t realize it was going paywall. The tool is worth it though.

    Any thoughts of making it more mobile friendly?

  17. Strolg says:
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    Have you ever done an analysis of what a pitchers Streamonator number before the start averages out for WHIP and ERA?

  18. Kap says:
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    It seems reasonable that streaks would average out over all players, but I wonder if there are certain “streaky” players where there are stronger correlations between the previous 3 games and the next game, over their career

    • Once u start trying to find players that fit a type, you run into sample size issues. I don’t think streaky is necessarily a trait that is static either – batting approaches change and may lead to more consistency as one ages

  19. Chad says:
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    Which side do you prefer?
    Lynn and Jones
    or
    Stanton

  20. Bryan says:
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    Please tell me what improvements I need in hitting,not worried about pitching(going to stream)11 team 5×5 roto.
    C-Zunino
    1b-Edwin
    2b-Rendon
    ss-Santana
    3b-Arenendo
    Of-A.Jones
    Of-Harper
    Of-Martin
    Of-Garcia
    Of-Ozuna
    1b/3b-C.Davis
    2b/ss-Wong
    Util-Hosmer
    Bn-Gyorko
    Bn-Souza
    Bn-Tomas
    Sp-Zimmerman
    Sp-Arietta
    Sp-Bauer
    Cl-Krod
    Cl-Benoit
    Cl-Cecil
    Cl-Mejia
    Cl-Hawkins
    Cl-Soria

    • Your offense looks pretty good to me. Don’t love Zunino and Chris Davis on same team. Definitely don’t need to compound that AVG drain with Gyorko. I would slot Wong at 2B while Rendon is out and find a MI with decent AVG/R.

  21. Whirlaway says:
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    Rudy,

    Lots of great insight here.

    Question: You joke about paying no attention to batter vs pitcher data, except for Goldy vs. Lincecum. When does batter vs. pitcher data become predictive? These two come to mind as I’m a Royals fan and I’m fascinated how these two can ‘own’ dominant pitchers:

    Billy Bulter vs. Justin Verlander: 34-84, .405/.468/.536. 5 2B, 2 HR, 9/13 BB/K.
    Alex Gordon vs. Max Scherzer: 14-39, .359/.479/.692. 4 2B, 3 HR, 9/8 BB/K.

    So is this minutiae or significant?

    Thanks man, great read.

    • Thanks. I don’t pay attention to batter/pitcher. I don’t know when it would become statistically significant – I just find it easier to ignore that variable. One less thing to research. All I care about is hittertron proj + playing time likelihood and lineup slot

    • And Now the Jon Lovitz Dancers! says:
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      @Whirlaway: like that fine print states, The Book showing one example of it, there’s a LOT of good essays, data showing even 84 at bats vs same pitcher for butler simply isn’t statistically significant. Probably too many variables but i would be interested in knowing whether or not certain “types” of pitchers against certain batters could be significant though.

  22. FrankGrimes says:
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    I feel like I should thank you again once a day sir

  23. Catcher Fever says:
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    Nice job. In my loooong time rotisserie – weekly lineup – league, the only factors I’ve found to give a marginal – but beneficial – advantage have been ballparks and lefty/righty matchups.

    Coors field is the easy one for hitters and avoiding pitchers.

    By lefty/righty matchups I mean the # of SPs a hitter will face that week, especially LEFTIES. For example, in 2013 I had a ton of success platooning Brandon Moss. When he faced 5-6 righties in a week, he was a god. When he had 6 games and 3-4 were against lefties, I benched him every time. It worked like a charm; his averages were significantly better in my lineup than his numbers overall.

    Also, pitchers’ ratios across the board tend to be much better at home than on the road… even if the home ballpark is a hitter-friendly park. Other than Rockies of course.

    • Catcher Fever says:
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      @Catcher Fever: An example of an SP I just took for my bench / spot starts: JA Happ.

      I would have thought before looking that his ERA & Whip were bloated from home games in Toronto last year. Not even close. Home ERA: 3.15. Road ERA: 5.67.

      That makes me feel about as confident as possible to start him at home in Seattle and giddy when he has 2 home starts in week.

      • I like Happ as a home start streamer too.

    • Yes, park and pitcher are key and reflected in SON. One thing that is clear from doing RHP/LHP splits (see top left box in player pages) that a number of Quality left-handed hitters are near or below-replacement level vs LHP and warrant platooning whenever possible

      • Catcher Fever says:
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        @Rudy Gamble: Cool thanks, I see the $/RHP and $/LHP now. I don’t know what average is but I’m pretty sure more is better !

        (I assuming its more geared to comparing individual players).

        • $1 is replacement for 12-team (should be available on waivers/FA). $11 is about average

  24. Derrick says:
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    Thoughts on this team in a 12-team, H2H, 5X5 (quality starts instead of wins). I didn’t have a 1st round pick because of a previous trade.

    C – Gomes
    1B – C. Davis
    2B – Gyorko
    3B – Arenado
    SS – Santana/Bogaerts
    OF – Cargo
    OF – J. Upton
    OF – B. Harper
    Util – Corey Dickerson

    SP – T. Ross
    SP – M. Latos
    SP – P. Hughes
    SP – Shoemaker
    SP – Hutchinson
    SP – Iwakuma
    SP – C. Carrasco
    RP – Cecil
    RP – Uehara
    RP – Boxberger
    RP – Qualls
    RP – Reed

    • I like the SP depth though I’d prefer an ace too. I would dump qualls for another SP. You have a lot of guy on my ‘do not draft’ list (Davis, Gyorko, cargo, upton, Ross, Latos) but seems fine if they deliver how others think they might. A bench bat will help on weeks the Rockies are on the road

  25. buffalo runner says:
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    Alex Rodriguez or Lonnie chisenhall.

  26. Bammers says:
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    Good stuff Rudy.. question for you. 5×5 standard roto 16 team dynasty league. I got the following offer: his Steve Pearce and Jason Kipnis for my Jordan Zimmerman and Dalton Pompey. Do you take it?

    • I like it. Not a fan of Pearce or Pompey for long term and Kipnis has a higher ceiling.

  27. Seph Meier says:
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    is there still love out there for Wil Myers? I have pick #141 and he is available…pull the trigger? other Outfielders out there include rusney, cain, Trumbo, werth, choo, rios etc..

  28. RicoSuave says:
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    NICE stuff Rudy!
    14 team H2H points league… I’ve been offered Goldschmidt and Carlos Gonzalez for my Edwin Encarnacion and Starling Marte… should I do it?

    who gets the better deal?
    thanks

    Trying to decide here…

    thanks again

  29. Johnny says:
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    This is awesome! Always wondered that.

    What do you think about doing the same for pitchers? Pitchers have hot and cold streaks just like hitters. Especially when the SON always ranks the same pitcher (Blanton a couple years ago) very high, but he continually gets pounded, it would be nice to see the correlation and effect of recent performance.

    Is there even enough data to this with SP? I personally like tailing hot SP until they get hit..

  30. Kerry Klug

    Kerry says:
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    What value do you place on batter vs pitcher when comparing hitters for daily leagues?

  31. Frank White says:
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    I have to drop either LaRoche or Werth. Which one should I drop? I need more pitching

  32. James says:
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    How do I sign in to the DFS bot. I have paid and subscribed?????????

    • Sky

      Sky says:
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      On the DFSBot page, click the ‘Subscription Options’ button and click the ‘Log In’ link where it says ‘Already have an account? Log In’. It’s in small’ish font, easy to miss.

  33. the swinging says:
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    “Conclusion: The Player Rater’s 360 ranking is overkill but I think Latos will be a negative play in 10/12-team leagues – either due to injury, reduced performance, or both. I do not like him at the NFBC / LABR spots but his downside is at least partially factored into his price. Hoping Grey drafts him out of spite in our RCL.”

    *slow clap*

  34. paul

    paul says:
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    Nice work Rudy. I need a yearly reminder to remain agnostic about streaky performance. It’s crazy how convincingly you think you perceive value in reacting to hot/cold streaks.

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