Developing a Strategy for Pitching Usage By: Dylan Blechner, Gareth Jobling, Kyle Liotta, Colby Olson, Nick Schloop Table of Contents 1. Framing the Problem 2. Thought Process 3. Hypothesis 1: Openers 4. Hypothesis 2: Optimal Pitch Count 5. Hypothesis 3: Number of Times Through Batting Order 6. Hypothesis 4: Relievers in High-Leverage Situations 7. Monte Carlo Simulation 8. Hypothesis 5: More/Fewer Starting Pitchers 9. Addressing Problems 10. Conclusion Framing the Problem
• MLB teams are in an era of creativity with pitching usage • Teams need to be smarter and more efficient with pitching usage to squeeze the most amount of wins out of their club • The days of using only 2-3 pitchers per game are in the past • New strategies have been used in recent years: • The Opener • Using best reliever in highest leverage situation • Taking starters out sooner Thought Process
• Due to the complexity of the prompt, we addressed it from many different angles • We came up several different hypotheses based on pitching strategies that have been used during the 2018 MLB season • Tested each hypothesis and looked to implement different strategies for each team based upon our results • Used 2018 data and 2018 MLB rosters to suggest which teams should use each strategy Hypothesis 1: Openers
• An Opener is defined as a pitcher who pitches the first 1-2 innings of a game, but averages fewer than 1.6 innings per outing • Several teams have begun to use relief pitchers to start games to good affect • We were able to find data on these 28 Openers and analyze their performance Hypothesis 1: Openers
• As can be seen below, the performance of Openers is not much better than that of their starting counterparts, but Openers are used to take the pressure off the next pitcher in line (“Follower”)
WPA wOBA Against FIP
Openers 0.0007 0.260 1.45
Starters 0.0019 0.293 1.00
• To put that in perspective, the most used “Follower” last season was Ryan Yarbrough (19) Hypothesis 1: Openers
• When looking at pitchers who followed an Opener vs. their performance as a starter, we can see that the Opener relieves stress from not facing the beginning of the order
WPA wOBA Against FIP
Followers 0.043 0.243 0.805
Starters -0.015 0.294 1.60
• Because of this trend, we feel that teams can utilize Openers as a strategy to help them win • Which other relievers can become successful Openers? Hypothesis 1: Openers
• The concept of the opener started with Sergio Romo • .206/.277/.384 against RHB in 2017
• .236/.333/.400 against LHB in 2017 • Openers tended to be very good against same-handed batters in 2018 • We used this same principle to come up with possible candidates for the Opener position for the 2019 season Hypothesis 1: Openers
• To find similar players, we ran a K-Nearest Neighbors Classification for both left-handed and right-handed relievers • We ran the model using the following variables: • Innings Pitches, Pitches Per Game, xFIP, wOBA • AVG, OBP, SLG for same-handed batters • The model showed us the most similar left and right-handed relievers that could be used as Openers Potential Righty Openers for the 2019 season
Name Team IP xFIP wOBA AVG (R) OBP (R) SLG (R)
Matt Barnes BOS 61.2 2.83 0.268 0.190 0.277 0.333
Chad Green NYY 75.2 3.19 0.273 0.221 0.271 0.366
Justin Miller WAS 52.1 4.23 0.272 0.207 0.256 0.375
Silvino Bracho ARI 31 4.18 0.275 0.203 0.280 0.358
Cam Bedrosian LAA 64 4.19 0.319 0.247 0.323 0.408
Wander Suero WAS 47.2 4.33 0.314 0.240 0.301 0.427
Tommy Kahnle NYY 23.1 4.29 0.411 0.289 0.439 0.511
Justin Grimm CLE 17.1 6.89 0.368 0.250 0.418 0.450 Potential Lefty Openers for the 2019 season
Name Team IP xFIP wOBA AVG (L) OBP (L) SLG (L)
Taylor Rogers MIN 68.1 2.94 0.202 0.178 0.248 0.180
Brian Duensing CHC 37.2 6.16 0.304 0.211 0.313 0.377
Brian Johnson BOS 38.2 4.53 0.286 0.220 0.291 0.360
Brett Cecil STL 32.2 6.47 0.370 0.305 0.379 0.483 Hypothesis 1: Openers
• We looked at each team’s first four batters to see if an Opener could be used to attack a team’s weak same-handed batting • Worst against lefties: • Tigers, White Sox, Royals • Worst against righties: • Orioles, Padres, Athletics Hypothesis 1: Openers
• Red Sox, Nationals, Yankees, Twins, Diamondbacks, Indians, Cubs, and Cardinals have relievers that can be used as Openers in the 2019 MLB season • These teams are different and may use Openers against different opponents and in different manners • Although it is a small sample size, we found that using an Opener leads to better team performance • As more organizations begin to implement the strategy we will be able to see the full effects Hypothesis 2: Optimal Number of Pitches
• Teams tend to have a scheduled time during a game when they plan to remove the starting pitcher • Teams are leaving the starting pitcher in too long • Teams should rethink their methodology when it comes to taking out the starting pitcher • League average IP by Starters = 5.4 • Yankees, Brewers, Angels and Rays all below average • Rays lowest at 3.9 IPS Hypothesis 2: Optimal Number of Pitches Hypothesis 2: Optimal Number of Pitches
• Ran a logistic regression, with the team result as the dependent variable
Variable Coefficient P-Value
Intercept 0.42625390 0.156093
Innings Pitched 1.03592556 0.00000
Pitches Thrown -0.02089610 0.029007
Batters Faced -0.25249651 0.00000
Pitches Thrown Squared 0.00019674 0.000594
• Results show that the more batters a pitcher faced, the less likely the team was going to win Hypothesis 3: Number of Times Through Batting Order
● As the game progresses, each batter faces a pitcher more and more times ● We wanted to see if this had some effect on the way a staff should be utilized ● In the subsequent analysis, the fourth time through the lineup is left out due to small sample size Hypothesis 3: Number of Times Through Batting Order
● Pitch Speed could be one of factors that influences pitcher effectiveness as they face batters more times throughout a game due to fatigue ● This may have a negative effect in some of the upcoming statistical trends Hypothesis 3: Number of Times Through Batting Order
● Second time through the lineup the wOBA decreases as pitchers start to throw more secondary pitches ● There is a significant increase in wOBA the third time through the order ● Starters should not face more than 18 batters in a game no matter the situation Hypothesis 3: Number of Times Through Batting Order
● The third time through shows the real difference as it is blown up from the previous slide ● Max velocity decreases ● Breaking pitches lose sharpness due to fatigue ● Leads to an increase in wOBA Hypothesis 3: Number of Times Through Batting Order
● Spin rate could have an effect as the game goes on ● Spin rate trends down as the pitcher faces more batters and throws more pitches which could affect the opposing teams run production as we just saw ● Decreased spin rate = less effective pitches Hypothesis 4: Relievers in High-Leverage Situations
• The best relievers on a team are usually called in for high-leverage situations to stop or prevent any damage from being done • Leverage Index quantifies pressure to show whether a pitcher is mainly used in high-leverage or low-leverage situations • The situations are put into 3 bins: Low: 0-0.85, Medium: 0.85-2.0, High: 2.0+ • A Leverage Index of 1 is considered average • We are testing to see if using top relievers in high-leverage situations benefits the team • Our data consists of the top 50 relievers by fWAR Hypothesis 4: Relievers in High-Leverage Situations
• Using high quality relievers in higher leverage situations leads to increased win probability • Using high quality relievers in extremely low-leverage situations is not effective because the team is already losing by a large margin Hypothesis 4: Relievers in High-Leverage Situations Hypothesis 4: Relievers in High-Leverage Situations
• It is beneficial to use top relievers in high-leverage situations • Putting a top reliever in the game for a high-leverage situation increases Win Probability Added • Teams should use top relievers for these situations and not save them for save situations • There may not be a save situation if the high-leverage situation is not taken care of Monte Carlo Simulation
• Monte Carlo simulation is a technique used to understand the impact of risk and uncertainty. A Monte Carlo simulator helps one visualize most or all of the potential outcomes to have a better idea regarding the risk of a decision • In our case, we are using a Monte Carlo simulation to simulate all of the games from 2018 • We wanted to choose one team from the 2018 season that could utilize the strategies mentioned to win more games Hypothesis 5: More/Less Starting Pitchers • The team we selected was the Minnesota Twins • The Twins used three starters consistently last season (Kyle Gibson, Jose Berrios, and Jake Odorizzi) and they will all be returning this season • Used Lance Lynn (20 GS, 102.1 IP) until he was traded at the deadline • By September, the Twins started using the Opener strategy with left-hander Gabriel Moya frequently • The Twins won 11 of their last 14 and the Opener was a big reason for the late season run • We found that Taylor Rogers could be a suitable Opener Simulation Assumptions and Limitations
• To simplify our results and make it easier to simulate MLB games, we came up with a few assumptions: • Simmed each matchup 162 times and the team with the greater win total was selected as the winner of each game • There are no relievers: we assume every starter pitches all 9+ innings (with the exception of Openers and Followers who pitch the first 2 innings and next 7+ innings respectively) • Opposing starters pitch in a 5-man rotation which goes from 1 to 5 and restarts for each cycle • The Twins rotation is a 5-man rotation, but the fifth man and fourth man are occasionally skipped for openers • Each team’s most-used lineup is used with the 5 most used starters • Fatigue and rest as well as fielding are not taken into account Hypothesis 5: More/Less Starting Pitchers
• Chose 18 games where the Taylor Rogers/Kohl Stewart combination could be used • In those games, the Twins were originally 9-9 • With the Opener strategy they improved to 13-5 • We also chose 15 games where the Gabriel Moya/Stephen Gonsalves partnership could be/was used • In those games, the Twins were originally 8-7 • With the Opener strategy, the Twins dropped to 6-9 • This was the case because Moya/Gonsalves were used in place of the numbers 1, 2, and 3 starters while Rogers/Stewart were used in place of the #5 starter Hypothesis 5: More/Less Starting Pitchers
• We continued the sim with the new pitching matchups to see if the Opener strategy provided some relief for the Twins’ starting pitchers and would give them a better overall record • The results were telling: both Kyle Gibson and Jose Berrios had a better simmed record than the 2018 regular season • Jake Odorizzi, Lance Lynn, and Fernando Romero each had a worse simmed record • The Twins finished with a simulated 75-87 record which was slightly worse than their 2018 record • The Twins had a worse Pythagorean win-loss record last season as well, showing that they overachieved Hypothesis 5: More/Less Starting Pitchers
Starter name Team record Sim team record • Because of these poor results Kyle Gibson 13-20 18-15 for Romero, we decided to remove him from the rotation Jose Berrios 15-17 18-14 and replace him with the two Jake Odorizzi 15-17 12-20 Opener pairs (13 new apps) • Lance Lynn 10-9 6-13 This improved the Twins record by 5 wins to 80-82 Fernando Romero 8-5 2-11 Hypothesis 5: More/Less Starting Pitchers
• The Twins performed better with more Opener appearances and we would expect this trend to continue with even more appearances • We found that shortening the rotation to 4 pitchers seemed to improve performance with situational Opener starts • This provides teams with pitching advantages where a top starter may face an opponent’s fourth or fifth best option • Based on our simulation, we feel that the Minnesota Twins should shorten their rotation to four starters and use Taylor Rogers and Gabriel Moya as Openers Addressing Problems
• While we have shown that all of these strategies can be used by MLB teams to optimize their pitching usage, we haven’t taken injuries into account • Difficult to predict injuries • We also did not take roster changes into account • Many teams added new players over the offseason and new minor league players will start their careers as well • We would suggest that minor league teams adopt the same Opener strategy to provide depth to major league teams Conclusion
• We found that the use of Openers positively affects performance • The variance in pitch totals creates little to be found from this hypothesis • The more batters a starter faces, the more likely the run production of the opposing team increases • Teams should use their top reliever in high-leverage situations • The rotation should incorporate the opener into certain situations as the season progresses based on matchups Questions? References
• https://www.fangraphs.com/leaders.aspx?pos=all&stats=pit&lg=all&qual=0&type=8&season=2018&month=0&seaso n1=2018&ind=0&team=0,ts&rost=0&age=0&filter=&players=0 • https://library.fangraphs.com/misc/li/ • https://towardsdatascience.com/the-house-always-wins-monte-carlo-simulation-eb82787da2a3 • https://legacy.baseballprospectus.com/sortable/index.php?cid=1819123 • https://github.com/junsooshin/baseballsimulator • https://www.baseball-reference.com/leagues/MLB/2018-starter-pitching.shtml • https://www.retrosheet.org/ • https://baseballsavant.mlb.com/statcast_search