Amy Langville, Professor of Mathematics, The College of Charleston in South Carolina & Tim Chartier, Chief Researcher, Tresata at MLconf ATL 2016

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Learning to Play Sports: Sports Analytics is an active and growing field. With large datasets from biometric devices and player tracking equipment, sports teams can benefit from techniques in data analytics and machine learning. This talk will discuss work in the areas of March Madness and game-to-game analysis. With the emergence of algorithms to study such dynamics as player performance and fan engagement, the collection of data also becomes paramount. Professional sports organizations have access to premium technology. This talk will also discuss how such work can be transferred to the college and secondary levels. Machine learning allows cutting edge technology to play from the bench.

Technologie

Learning to Play Sports
ML in sports analytics
Dr. Tim Chartier
Tresata
Davidson College
tichartier@davidson.edu
Dr. Amy Langville
College of Charleston
Dept. of Math
LangvilleA@cofc.edu
@timchartier

Play from bench
data availability
general interest
(a.k.a. cool factor)
domain knowledge

Apply here
Picture credit: http://orlandonest.files.wordpress.com/2011/03/2011-march-
madness-bracket.gif

How do we do?
• ESPN Tournament Challenge: > 4 million
brackets!
• 1st round correct choice = 10 points
• nth round correct choice = 2*(previous round)

Method 1: crowd source
• 2009 – best bracket – 97%
• 2010 – best bracket – 99%
• 2014 – national media led to thousands of
brackets on: marchmathness.davidson.edu

Method 2: learn sports
• vary parameter weights to optimize ESPN
score or prediction rate
• subtlety: not all seasons are equally predictive

Method 3: mad web
10 years, 50,000 games

Application 2: Cats Stats
Analytics for college teams to support coaching.

sports analytics keys
• coachable
• consumable
• understandable (informed opinion)

impact: coaching
“It kind of blew us away…it really opened our eyes...”
– Matt McKillop, NYT

impact: off-season
Player Poss. TO% OR% EFG% 2P% 3P%
Brian
Sullivan
77 14.3% 20.0% 65.6% 67.4% 40.0%
without 56 23.2% 20.8% 55.3% 42.9% 47.1%

MasseyRatings.com
column 1 = date of game as measured as days since 1/1/0000
column 2 = date in YYYYMMDD format
column 3 = team 1 index
column 4 = team 1 home field (1 = home, -1 = away, 0 = neutral)
column 5 = team 1 score
column 6 = team 2 index
column 7 = team 2 home field (1 = home, -1 = away, 0 = neutral)
column 8 = team 2 score

Tresata Data
For network analysis, Tresata added:
• seed
• coach’s Madness history
• kenpom.com statistics
• every season game (and added game stats)
What can we learn from about 50,000 games?

Data needed
• ESPN bracket challenge scores for past years
• injuries for every game
• score with 2 min or 4 minutes left
• learn from Vegas odds
• biometric data

• If we remove a team and it highly
affects reranking, what can we
learn about such a team for March
Madness?
• How can Buddy Hield light up March
Madness?
• Compare Jack Gibbs to Stephen
Curry in college play.
media ?’s

Rankability
Data
Apps Amazon products
Netflix movies
Financial networks
Teams

Intuitive Ideas
one extreme
Dominance graph
(very rankable)
Random graph
(less rankable)
other extreme

Inconsistency
Uparcs in a rank-ordered graph
5 uparcs

Inconsistency
Uparcs in a rank-ordered graph
Minimum Violations Ranking
3 uparcs

Inconsistency
BUT this measure of rankability
is tied to the ranking.
March Madness 2008
sorted by Massey rating
uparcs = 27.2%
March Madness 2014
sorted by Massey rating
uparcs = 26.9%

Goal
k-cycles
Create a rankability measure that is independent of ranking.
2-cycles: 1-2-1
2-1-2
5-cycles: 1-2-3-4-5-1
2-3-4-5-1-2
3-4-5-1-2-3
4-5-1-2-3-4
5-1-2-3-4-5

Goal
k-cycles
Create a rankability measure that is independent of ranking.
2-cycles: 1-2-1
2-1-2
5-cycles: 1-2-3-4-5-1
2-3-4-5-1-2
3-4-5-1-2-3
4-5-1-2-3-4
5-1-2-3-4-5
4-paths: 1-2-1-2-1
2-1-2-1-2

Future Work
If a dataset is not very rankable, which edges should
we add to the graph to improve its rankability?

earn to play sports
data questions applications

questions?
Picture credit: http://www.trendir.com/ultra-modern/

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