There has been a lot of interest on the site¬†and in the forums¬†involving streaming pitchers. ¬†As many of you know, I value SP more than the average drafter but I am not completely averse to streaming – particularly on teams in shallower league formats (10-12 MLB) with small benches and when I have lost SPs to injury (like RCL where Beachy and Colby are toast). ¬†This is about the time of year when I like streaming best as most teams are at or ahead of pace for innings/games started so there is less competition in free agency.
Inspired by the season-to-date¬†success of frequent commenter/forum poster and occasional contributor Fred, I went about creating a tool and testing/developing a methodology for identifying viable streaming candidates. ¬†The tool is at razzball.com/streamers¬†and we will add a link to it on the homepage.
The variables I ended up using are as follows:
- FIP Index – This is just the league average FIP (4.00) divided by the pitcher FIP * 100. ¬†So a pitcher with a 3.00 FIP has an index of 133 (4/3*100) and is 33% better than average. ¬†Going into the analysis, I assumed that the primary variable would likely be a measure of the pitcher’s skill. ¬†As many of you are aware, FIP (Fielding Independent Pitching) has proven to be a better predictor of future ERA than past ERA as it focuses on the aspects that pitchers control most (HRs, K, BB) and excludes the areas where pitchers have lesser control (BABIP). ¬†Furthermore, it is easy enough to calculate so I can automate it easily. ¬†xFIP (which replaces a pitcher’s HR/rate with league average) and SIERA (a more complicated variation of FIP) were considered but I do not think they provide sufficient improvement over FIP to justify all the complexity. ¬†(I’m not alone in that sentiment.). ¬†In cases where the pitcher has thrown less than 100 IP, I have credited them with an index of 80 for the IP difference and took the weighted average of the two (e.g., a pitcher with an FIP index of 200 in 10 IP would receive 90 IP of 80 and average out to 92). ¬†This is probably too conservative but I’d rather be too conservative with pitchers with lesser track records than too liberal.
- Last 20 Game FIP Index – This takes the pitcher’s FIP for the past 20 games. ¬†While this metric correlates fairly high with FIP Index (most good/bad pitchers are going to be high/low in both), it does account for pitchers who are on hot/cold streaks. ¬†If a pitcher hasn’t pitched in the last 20 games, I just credit them with their season FIP. ¬†To avoid this index becoming too distortive, I put a ceiling at 200 (e.g., any Last 20 Game FIP of 2.00 or under gets a 200 whereas, uncapped, a FIP of 0.50 would get a 800, et al.)
- Park Factor – This index isolates the hitting environment for the park in which the start is taking place. ¬†The higher the index, the worse it is for the pitcher. ¬†I have calculated these indexes using a weighted average of 2008-2011 Runs/Game in each park (from ParkFactors.com) and 2012 Runs/Game from ESPN. ¬†I planned on just using the 2012 Runs/Game but the smaller sample size (~50 games) seems too volatile. ¬†I tested the 2008-2011 factor, the 2012 factor, and the weighted factor against a 4 game test data set¬†and the weighted factor correlated best to pitcher ERA/WHIP.
- Opponent Home Park Adjusted Run Index¬†– This index aims to isolate the offensive prowess of the pitcher’s opponent. ¬†It is key to isolate the team’s offense from their home park to avoid double-counting Park Factor. ¬†Since I do not have access to a data feed for importing Home/Road offense splits, I did this by dividing each team’s Runs/Game against the following factor (% of games at home * Park Factor + % of games away * 100). ¬†So teams in offensive parks like the Rockies and Yankees have their Runs/Game decreased and vice versa. ¬†The team indices should mirror their Road stats (as this usually averages out to close to a neutral-offense park). ¬†The reason I chose Runs/Game vs. OPS or another offensive metric is to remain consistent to the other three indices (which helps since indices are driven by the denominator).
- Home Start vs. Road Start¬†– Pitchers generally pitch better at Home vs. Away – even after factoring in the park. ¬†Courtesy of ESPN, here are the stats: ¬†2011 Home/Road/Average ERA: ¬†3.82/4.07/3.94, 2012 Home/Road/Average ERA: ¬†3.80/4.21/4.00. ¬†So pitchers roughly do 5% better than average at Home and 5% worse than average on the Road.
The formula for determining a pitcher’s score came from doing a multiple regression analysis of these four variables against WHIP (which I considered the most predictive stat to gauge a stream’s success).
The correlations to ERA/WHIP for each stat based on a 4-game test are in parentheses – see here for the worksheet. ¬†The higher the correlation – the better the variable is at predicting ERA/WHIP):
FIP Index (19.4%/20.1%)
Last 20 Game FIP Index capped at 200 (10.8% / 16.5%)
Park Factor (11.7%/7.2%)
Opponent Home Park Adjusted Run Index (3.8%/5.3%)
Home/Road Start ¬†– crediting +5 for Home, -5 for Road (7.7%/9.4%)
The formula from the regression analysis is really mathy – if you really really care, I explain it in more depth on the ‘Formula’ tab of the worksheet linked above. ¬†The key points:
- The correlation between a pitcher’s resulting index and their ERA/WHIP is 23.1%/25.1%¬†which does improve upon using FIP alone (19.4%/20.1%). ¬†This is by no means fantastic – you can read this as the score helped explain only 25% of a pitcher’s WHIP with the other 75-80% driven by chance/luck. ¬†But this type of skill/luck split is common in Fantasy Baseball and one that keeps me really humble as an analyst (see here to see my analysis of pre-season rankings and how they correlate with RCL Team success¬†– the predictiveness of rankings is about as humbling as it gets).
- Interestingly, despite the fact that FIP Index is the best single indicator, the Park Factor is the index with the highest weight in the formula. ¬†Ignoring the Home/Road start variable (which is not an index and thus is on a different scale when determining the coefficient), the weights are: ¬†31% FIP Index, 14% Last 20 Game FIP Index, 39% Park Factor, and 17% Opponent Home Park Adj. Run Index.
Variables I considered but did not use include:
- Home/Road splits for Pitchers¬†– In the roundups, we often reference pitchers with extreme home/road splits like Tommy Milone in 2012 or Clayton Richard in 2011. ¬†The reality is that – once you adjust for the park – these splits are not very predictive because pitching at Home/Away really isn’t a skill. ¬†So, yes, a Padre pitcher fares better at Home vs. Away but that’s reflected in the park factors as well as the Home/Road adjustment. ¬†Here is an analysis I did looking at 2011 vs. 2012 Home/Road pitcher splits. ¬†If a pitcher has shown consistent and considerable splits in Home/Road over multiple seasons, I can see this as potentially valuable criteria – I just do not think it has shown to be consistent across enough pitchers to warrant inclusion.
- Day/Night splits for Pitchers –¬†Same rationale as why I disregard Home/Road splits for pitchers. ¬†Fun example – this year, Hiroki Kuroda leads the majors in day game ERA among pitchers with 30+ IP – an amazing 0.00 ERA (and 0.67 WHIP). ¬†His night ERA is 4.23. ¬†It’s as if he has the opposite of Josh Hamilton’s daylight-sensitive blue eyes! ¬†Given this huge split, you would think that Kuroda is a better day pitcher vs. night pitcher. ¬†For 2009-2011, in 108 IP Kuroda’s day ERA is 3.56 which is WORSE than his night ERA of 3.29. ¬†Either Kuroda has suddenly picked up a daytime pitching skill upon donning Yankee pinstripes or this is just an anomaly. ¬†If you believe the former, feel free to overlay that as your own personal variable for considering streaming pitchers.
- Left/Right splits for a Team’s Offense¬†– This one is interesting. ¬†I do not have these stats at my disposal but there could be something here given my assumption that some batters exhibit consistent left/right matchup splits.
- Pitcher K-rate, O-Swing %¬†– Certainly valuable for identifying pitcher success but K’s are accounted for in FIP. ¬†No reason to double count this.
- Pitcher BB-rate –¬†Certainly valuable but BBs are accounted for in FIP.
- Pitcher vs. Opponent Previous In-Season Matchups¬†– I do not think this holds up as a predictive variable mainly because 1) small sample sizes and 2) there are too many cases where it is the first matchup so it cannot be applied equally across all starts. ¬†This FanGraphs post by Dave Cameron talks about the fallacy of reading too much into pitcher/batter matchup data – I think pitcher/team matchups are just an extension of this. ¬†That said, I can see using this subjectively – no reason to go forward with streaming a pitcher if you do not feel comfortable. ¬†Personally, I do not like it when a pitcher has to face a team twice within short succession. ¬†Is this statistically valid? ¬†Probably not.
- % Owned¬†– This would not be a variable – it is just useful reference data that could help in identifying pitchers more likely to be available for streaming. ¬†Unfortunately, this is not something that we can automate and I do not think we have the requisite rights to this data. ¬†Maybe one day we will have a true techie on staff who can figure this out with Yahoo’s API.
Given the relatively low correlations between the Stream-o-nator’s Score(~25%) and pitcher ERA/WHIP, there would seem to be significant room for improvement. ¬†Perhaps in time, we end up adding extra variables, tweaking the current variables, or tweaking the weights of the current variables. ¬†But I would guess that any additional gains will be marginal given that four positively correlated variables only add an additional 5% to Season FIP’s 20% correlation.
My general advice on pitcher streaming is that there are ways to improve your chances of successfully streaming but there are no sure bets. ¬†Some are in your control (picking pitchers with the best chance of success), some are not (whether other people in the league are also streaming). ¬†If you are expecting to lead the league in ERA/WHIP via streaming, you are banking on a statistical long-shot. ¬†It’s possible (as Fred has achieved season to date) but statistically improbable nonetheless. ¬†If you can manage to get production equivalent to average in your fantasy baseball league with streaming, I think you are ahead of the game.
Hope this is helpful and thanks to Fred and Awesomus Maximus for your feedback during the testing stages.