Mixed strategies in baseball Part 1
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Transcript of Mixed strategies in baseball Part 1
Mixed Strategy Nash Equilibrium in Baseball: The 2-1 Count
Greg PowellMasters of Applied Econometrics
Research Motivation• How can pitcher’s mix their pitches optimally when hitters have the
advantage in the count?• Interest in game theory, strategy and baseball• Real Time Human Behaviour• What conditions lead to optimal play?• Experimental research overall to reject MSNE. Do professional athletes
use MSNE?• Always wanted to examine the rich, freely available “Gameday” dataset• See what insights can be obtained• 2-1 and one count is an advantage to the hitter. Is there a strategy
where a pitcher can limit damage by the hitter?• Improve my own hitting approach!
Baseball Overview• Major League Baseball
– NY Yankees • $206 million Payroll • $5.5 million median player salary
– Pittsburgh Pirates • $34 Million payroll• $450K median player salary
• Highly paid/motivated players – High stake games– BIG BUSINESS
• 30 Teams, 162 games each (plus playoffs). Lots of heterogeneous and homogenous data.– “Baseball Everyday!”
• Amazing number of baseball literature both published and online (both statistically based and otherwise). http://content.usatoday.com/sportsdata/baseball/mlb/salaries/team
Quick Baseball Terminology!• 2 (Balls) - 1 (strike)– What is a Strike/Ball?– Baseball – Ideal for
statistical research as there are discrete outcomes to each play
– What is a Plate Appearance?
Quick Baseball Strategy
– Pitch Types– Pitcher/Hitter Strategies– 2-1 - Considered a
“Hitter’s Count” or a “Fastball Count”
Methodology – MSNE
• Mixed Strategy Nash Equilibrium– We can see in this
matching pennies game (sigh) that there is no pair of pure strategies that players would switch to if they knew what their opponent would do.
– Players are therefore indifferent to opponent strategy
MSNE B Heads B Tails
A Heads +1,-1 -1,+1
A Tails -1,+1 +1,-1
Methodology – MSNE & Baseball?
• MSNE & Baseball- Zero Sum Game- Simultaneous moves
(mostly!)-Weinstein-Gould Models an
M x N matrixM,N = # pitches
- In Major Leagues, Batters have access to full scouting histories – they will know what pitch types pitchers have in their armoury.
Batter
Pitcher
1 “Think Fastball”
2 “Think Changeup”
3 “Think Curveball”
1 Fastball 11 12 13
2 Changeup 21 22 23
3 Curveball 31 32 33
Literary Review - MSNE• Experimental Research
– In the Lab– Difficult to prove MSNE – why?– Lots of 2*2 Games and matching pennies– Aren’t we sick of matching pennies???!!!!– Maybe we’d detect MSNE in a World Matching Penny Tournament?
• Research in Professional Sports– Soccer (Chiaporri) – Penalty Kicks
• Not Much Data
– Tennis (Walker and Wooders)• Serve/Return of Serve
– Baseball (Weinstein-Gould)• Data collected by humans• MSNE for Pitch outcome. Not for measures of OnBase and weightedOBA• Very ambitious – analyses the first pitch and also the outcome of the plate appearance
Data
• Data from MLB “Gameday” Database • Collected using the Pitch F/x System• 2009-2010 Data – most games included• 72,445 2-1 Count pitches represented by:– 965 Batters– 805 Pitchers
Data – Summary Statistics• Pearson tests show that there IS a statistically significant and
different strategy in a 2-1 count as compared to 0-0. To be expected.
Methodology
• Initial Multinomial Logistic Regression – 72,445 Rows
• 805 pitcher dummy variable columns• 965 hitter dummy variable columns
–
Test the hitter dummy variables• Null Hypothesis H0: Hitter 1 = Hitter 2 = Hitter n = 0• If we fail to reject it shows that the pitchers have an indifferent or
mixed strategy toward hitters• Intuitively this makes sense
Next Steps
• Find a tool that will run my initial multinomial regression analysis
• Test the null hypothesis• Next step is to look at pitcher-hitter
combinations• Weinstein-Gould -