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Table of Contents

WhatIfSports.com Overview

Challenges with Simulating Baseball

Plate Appearance Decision Tree

Or “Improving the log5 Normalization Model for Batter/Pitcher Matchups”

Pedro vs. Ruth (mostly second presentation)

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About WhatIfSports.com

February 2000 - Launched in Cincinnati with SimMatchup

2001 - SimLeague Baseball (like Strat-o-Matic) and Basketball; Paul Bessire runs free leagues

2002 – SimLeague Football and Hockey; Paul wins own baseball league with “Streaking Ho-Hos”

2004 – Hoops Dynasty and Gridiron Dynasty; Paul joins WIS part-time “between school”

2005 – WhatIfSports.com acquired by FOX Interactive Media; Paul comes on full-time

2006 – Hardball Dynasty and Clutch Racing Dynasty; All simulations rewritten with Paul’s help

2008 – FC Dynasty

Present – 600,000+ registered users, part of FOX Sports TV group

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Sports Simulation

Play-by-play

A “play” means something different for each sport

Probabilities for every individual outcome

Random number generation

Pitch-by-pitch (or basketball/hockey pass-by-pass) not needed

Account for every possible statistical interaction during a game

Can be recreated quickly

200+ games/second

All data tracked

Every outcome is different

Boxscore (link)

Many relevant applications (second presentation)

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Baseball Challenges

Missing Player Data

Defensive Metrics

Ballpark Effects

Era Adjustments

Assigning Value (Salaries ~ RC27# * PA or ERC# * BF + Fielding + Extremes)

Career “Seasons” (Pujols #3 in career $/PA, Musial #16; Gibson #31 in $/IP)

Fatigue (Projected PA vs Actual PA/162 or Projected IP & GP% vs Actual IP/162 & Historical GP%)

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Missing Player Data

Typically solved with Regression

Linear: Pitchers’ 2B or 3B per hit allowed or Pitches Thrown per BF
Multivariate: Ballpark Effects
May be Era and/or Ballpark Adjusted

Discriminate Analysis/Cluster Analysis

Catcher’s Arm Ratings
Basketball Positional Effectiveness

Fitting to a curve/distribution

Player Generation and Development
Assigning Ratings or Grades

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Significant Stats ( # has missing data)

Pitchers

HBP/BF
BB/(BF – HBP)
OAV
1B/Hit Allowed
2B/Hit Allowed # (regression)
3B/Hit Allowed # (regression)
HR/Hit Allowed
K/Out # (regression)
GO/FO # (regression for GO)
BF # (approx. ~ outs + hits + bb + hbp)
Pitches Thrown/BF # (regression)
Relative Range Factor # (WIS formula)
Fielding Percentage # (fit to curve for grade)
Handedness (historical impact)
Ballpark Effects # (multivariate regression)
League Averages

Hitters

HBP/PA
BB/(PA – HBP)
AVG
1B/Hit
2B/Hit
3B/Hit
HR/Hit
K/Out # (regression)
GO/FO # (regression for GO)
PA
Relative Range Factor # (WIS formula)
Fielding Percentage # (fit to curve for grade)
Catcher Arm Rating # (discriminate analysis)
CS% (Runner) # (regression for CS)
Speed Rating # (WIS forumla)
Handedness (historical impact)
Ballpark Effects # (multivariate regression)
League Averages

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Insignificant Stats

Pitchers

Wins

Losses

Saves

Holds

Complete Games

Shutouts

ERA (kind of – 2B and 3B approx)

Unearned Runs

Games Started

Pitch Types

Performance in Counts

Other Situational Stats

Hitters

Runs (kind of – in Speed Formula)

GIDP (kind of – in Speed Formula)

SF (kind of – in PA, but also situational)

SH (kind of – in PA, in but also situational)

SBA (kind of – attempts, but also setting)

Performance in Counts

Other Situational Stats

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WIS Relative Range Factor

Range Factor

Important because range can turn hits into outs and outs into hits

Generally defined as (Putouts + Assists)/(Innings/9)

Reliant on many factors

Wildly inconsistent across eras

Does not include errors

Need another metric…

WIS Relative Range Factor

Similar to Bill James RRF, but not as robust (data limitations)

Approximates plays made/possible plays made

Used to approximate + and – plays

Includes errors

Era-adjusted

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RRF – Best (Min. 80 Games)

Position	Player	Season	Team	RRF
1B	Hal Chase	1919	New York Giants	12.7
2B	Billy Goodman	1952	Boston Red Sox	7.2
SS	Dave Bancroft	1922	New York Giants	7.1
3B	Buddy Bell	1981	Texas Rangers	4.1
OF	Darin Erstad	2002	LA Angels	3.7
OF	Mike Cameron	2003	Seattle Mariners	3.6
OF	Taylor Douthit	1928	St. Louis Cardinals	3.6
P	Greg Maddux	2002	Atlanta Braves	3.8

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Ballpark Effects

LINK

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Ballparks – Extremes (Min. 3 seasons)

Effect	Ballpark	High	Ballpark	Low
Hits	Coors Field	1.182	Petco Park	.908
2B	Baker Bowl	1.291	Dodger Stadium	.795
3B	Palace of the Fans	1.868	Great American Ballpark	.523
HR_RF	Coors Field	1.374	Municipal Stadium	.636
HR_LF	Coors Field	1.385	Municipal Stadium	.634
Runs (unused)	Coors Field	1.380	Petco Park	.830

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PA Decision Tree - Normalization

Every step in PA uses modified* log5 normalization (Bill James AVG example):

H/AB = ((AVG * OAV) / LgAVG) /

((AVG * OAV) / LgAVG + (1- AVG )*(1- OAV)/(1-LgAvg))

Where, LgAVG = (PLgAVG + BLgAVG)/2

2000 Pedro vs. 1923 Ruth Example:

H/AB = ((.393 * .167) / .2791) /

((.393 * .167) / .2791+ (1- .393)*(1- .167)/(1-.2791))

Where, LgAVG = (.283 + .276)/2 or .2791

Result = .2504

* Modified due to a flaw in the assumption above that the batter and pitcher carry equal (50/50) weights on each possible outcome of the PA event. Also accounts for handedness and ballpark.

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PA Decision Tree – Steps 1*

Plate Appearance

Unusual Event

(IBB, WP, PB, SB, CS, SH,

Hit and Run, Pickoff, Balk)

Normal PA

HBP

(per PA or BFP)

Not HBP

(per PA or BFP – HBP)

At Bat…

* No ballpark or handedness adjustments made yet.

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PA Decision Tree – Steps 2

At-Bat

Out

Hit…

(AVG vs. OAV)*

Strikeout

(K/Out)

Normal

(Logic to determine direction

and GO or FO)

Hit

(Poor Play)

Error

(Fielding Percentage)

Normal

* Historical handedness adjustment and ballpark hits multiplier used.

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PA Decision Tree – Steps 3

Hit*

Normal – In Play

HR*

(HR/Hit)

Out

(Plus Play)

Normal Hit

3B*

(3B/Hit * multiplier

for lost HR)

2B*

(2B/Hit * multiplier

for lost HR)

* Ballpark multipliers used.

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PA Decision Tree – Matchup Weights

Addresses previous 50/50 assumption using League-Adjusted Variance to form batter and pitcher weights for each step:

	HBP/PA	BB/(PA-HBP)	H/AB	K/(OUT)	HR/HIT	2B/HIT	3B/HIT
Pitcher%	47.8	43.5	46.7	45.6	39.7	15.2	11.6
Hitter%	52.2	56.5	53.3	54.4	60.3	84.8	88.4

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Matchup Weights: What does this mean?

Batter always has more control (even with HBP and BB)

Makes final decision (Swing or not)
Dictates strike zone
Less consistent

Doubles and Triples are (mostly) out of pitcher’s control (BABIP)

Does not necessarily batting is more important

9 vs. 1
Fewer pitcher outliers means elite pitchers are more valuable

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PA Decision Tree - Normalization

Batting Average Example using Matchup Weights:

H/AB = ((1.066*AVG * .934*OAV) / LgAVG) /

((1.066*AVG * .934*OAV) / LgAVG + (1.066- 1.066*AVG )*(.934- .934*OAV)/(1-LgAvg))

Where, LgAVG = (.934*PLgAVG + 1.066*BLgAVG)/2

2000 Pedro vs. 1923 Ruth Example (with handedness):

H/AB = ((1.066*.393 * .167 * .934) / .2795) /

((.393 * .167) / .2795+ (1- .393)*(1- .167)/(1-.2795))

Where, LgAVG = (1.066*.283 + 0.934*.276)/2 or .2795

Result * Handedness = .2502 * 1.045

Final Result = .2614

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Thanks

Questions? – @lunch or after second presentation

Email: PBessire@WhatIfSports.com

Phone: 513-291-0321

See me for business card with promo code