SSAC Recap Day 1

For those of you that missed the 2014 MIT Sloan Sports Analytics Conference, I wished to provide a recap.  Here are some shorthand notes from Day 1.
Opening Remarks: Daryl Morey (Geek Elvis) and Jessica Gelman

The theme for this year’s conference is ripple to revolution, describing how the field of analytics has grown in the sports world over the past few years, thanks in part to this conference. It started as a small field and known has become an integral part of competition. About 2,000 people were in attendance for the conference, which had approximately 1,000 on the waitlist alone. 10% of the attendees are international; there are 800 students from 180 institutions, and 360 sports organizations in attendance. The “heart” of the conference is the 26 panels offered. There are also four innovative areas to highlight:

1. Research Papers- with over 300 submission and 8 finalists

2. “Evolution of Sports” Talks

3. Trade Show Blitz- Start Ups Pitching Ideas to Venture Capital/ PE Firms

4. Competitive Adjustment: presented by industry experts



Panel #1: Athlete Analytics: Instrumentation, Training, and Injuries
Panelists: Shira Springer (moderator), Andrew Luck, Matthew Hasselbeck, John Brenkus, Adir Shiffman, and Qaizar Hassoujee

1st topic was on the evaluation of NFL Combine Process - Hasselbeck had interesting experience- was not invited to it after entering draft as Junior, was in same draft as Ryan Leaf and Peyton Manning

A.Luck- “There is merit to the interviews and psychological testing done”, wants to see as a QB more football specific movements as part of combine, such as simulating 2 minute drill, how to quantify focus and clock management

Brenkus- interesting to note that the 40 yard dash does not really matter for wide receivers- look at times of Jerry Rice and Larry Fitzgerald, change of direction and stopping is more important - for quarterbacks an important metric is concerning their release, not overall arm strength, ex: Kaepernick tested high for quick release

Shiffman- the real challenge is to provide data that is valid, 3rd party testing is needed

JB- it’s all about context, the defensive rookie of the year the last three years has done well in the combine - Florida State and its injury prevention use of analytics with performance maximization - great success was no soft tissue injuries at Combine this year

AL & MH: see it as a hindrance to wear performance-wearing tracking technology, too bulky - This year the Colts tried out different wearables, such as corrective posture shirts and masks that simulated altitude play

S: if athletes are aware that they are wearing technology, then the technology is a failure- they are conscious of it - most of the early adopters of new technology are the teams that are on the cusp of greatness and cannot buy additional success in free agency - baseball is most hesitant to adopt new technology, have very traditional approach

JB: bat speed actually goes down if you swing a weighted bat in on deck circle, argues that players should be swinging a whiffleball bat instead - NASCAR seen as head of analytics- always looking for ways to shave 1/100th of second

MH: we are always concentrating on studying the analytics on the opponent, might need to look at data on ourselves more

JB: there exists a generational gap when thinking about different levels of education on technology, e.g. some coaches are still very old school

How they all see future of this field

S: in- ball technology, inertial movement

AL: invisible sensors

MH: hydration levels testing, head impacts

Q: college level, sport specific, fan engagement

JB: defining standards Colts use analytics most in terms of 3rd down blitz tendencies and red zone situations Buzz word: CONTEXT the “holy grail” is predictive analytics

What they would like to see measured

AL: pitch count/ arm throw count and analysis

S: position-specific data

MH: recovery methods- which are best

JB: sport psychology

Final comment by Hasselbeck: would be interesting to put a heart rate monitor on Adam Vinatieri when he is about to kick game winner vs. all other kickers in league



Panel #2: Man & Machine: Real Time Data and Referee Analytics

Panelists: Hank Adams, Michael Bantom, Dan Brooks, Mike Carey, Paul Hawkins, Tom Penn (moderator)

Bantom: most important thing is for referees to maintain their focus over emotions

Adams: created SportVision, which is used for 1st down lines in football

Hawkins- sports of cricket, tennis, and soccer have real time computer decisions, have a signal that is relayed to the referee’s watch when ball crosses goal line in soccer (goal to increase the speed in soccer) - goal for referees and technology creators is to not get noticed, Example: arguably best tennis match of all time 2009 Wimbledon Final (Nadal vs. Federer)- was a critical line call play that was called correctly Brooks- sorts through the SportVision data and maps calls of umpires in MLB, some people are unhappy with inconsistencies

Bantom: NBA has log of every call made in the last 10 years and they rate each one as correct or incorrect, looking for next year to have a centralized location for calls and replay reviews

Carey: issue of player safety, NFL is “game of inches”- get tough calls with “double action”, where knee hits, ball hits and breaks plane - there has been an adaptive habit among players of how to approach tackles by not hitting with the helmet

Hawkins: issues in soccer with cheating in regards to diving vs. what is actually a genuine foul - future may look at review of red cards and other fouls to possibly overturn, want to be proactive with approach rather than reactive, Example: Lampard goal that was overturned in the England vs. Germany match in World Cup Carey: still uses rubber bands on each of his fingers during games to keep track of what down it is - “most difficult call is catch/no catch” - again it’s a game of inches- there is human judgment error when it comes to marking where a player is down on a field, where the chains are placed for the 1st down, how that compares to the SportVision computerized line we see on TV, is chain actually precisely 10 yards?, etc.

Hawkins: in soccer, it would be easier to judge call if it was based on whether middle of ball crossed goal line for goal

Brooks: catcher framing in baseball is arguably worth up to 20 wins a season- where you are fooling umps, what is value of Molina brothers? - would be hard to apply technology that soccer uses for NHL goals due to speed of puck, how much of puck crosses line, etc.

Bantom: in NBA, there is great transparency, as fans are able to see what officials see in terms of replay reviewing - However in MLB, interesting that there is no explanation of calls by umpires to the fans

 Panel #3: College Football’s Playoff Selection Dilemma Presented by ESPN

Panelists: Jeff Bennett (moderator), Dean Oliver, Alok Pattani, Brad Edwards, and Chris Fallica

- ESPN Stats & Info provides metrics such as expected points and probability models to help sort out Playoff selections - also includes strength of schedule calculations and adjusted ratings of team strength - going past the “eye test”, looking at how the game played out, Ex: FSU was up 42-0 in 2nd quarter

- 12 committee members comprising selection committee- How many of them are watching games/ which ones are they watching? ALL members are over 50 years old- cause for concern????

Key distinction is between “Best” and “most deserving” team- last year’s title game: Alabama vs. Notre Dame (undefeated, what did their resume look like, were they deserving??) - all panelists agree that Notre Dame would have been 4th seed in playoff

BIGGEST PROBLEM: determining who is 4th seed - looking at polls does not tell the story necessarily, Example: Gonzaga in NCAA Tournament last year, #1 seed (was #1 in AP Poll), with AP Poll you have to reassess every week win probability

- can look at metrics now such as final score margin, other team stats, average in-game win probability at different points in game

- Strength of Schedule is relative to perspective, there are different ratings and systems of how to configure SOS

The March Madness Selection committee is provided with a “Nitty Gritty” sheet when enter meeting with bunch of different stats and columns on teams - CFB Playoff Committee will be provided with NOTHING 

Panelists Now Had Activity Where Audience had chance to vote given blind resume tests from previous years to see who they would vote in/out of playoff system, would compare with panelists’ opinion and what the numbers said
First talked about FSU this year and looking at performance vs. level of competition - Loss Tolerance- Was actually found that it would be harder to go 12-1 with Auburn’s SOS vs. going 13-0 with FSU SOS

Scenario #1: revealed to be 2008 Alabama (only loss to #1 Florida in SEC Championship) vs. USC (conference champs)- numbers favor Alabama

Head to head matchups were a high priority for the panelists

#2: Stanford (11-1) vs. Oregon (11-2) in 2011- Oregon destroyed Stanford in regular season, votes and numbers favored Oregon

#3: 2013 4th seed: Alabama vs. MSU vs. Stanford- audience mostly voted to leave out MSU, but numbers say Stanford would be left out

Panel #4: Beyond the 4-4-2: Soccer Analytics

Panelists: Taylor Twellman(moderator), Steven Houston, Robbie Mustoe, Jim Pallotta, and Paul Neilson

 Latest development in soccer analytics: rise of technical scouts and positional data, future is with physiological and social fields - interesting that there is rise in U.S. owners in Europe (Pallotta with AS Roma as example)

Look at economics with transfers and acquisitions- Is Wayne Rooney really worth $500,000/ week? Tottenham was able to acquire Gareth Bale for 7 M pounds and later sold him to Real Madrid for about 87M pounds

Pallotta: At Roma, strategy is to build strength in the midfield, middle of pitch and with defense (which has given up least amount of goals in Europe this year) - notion of buying one star player and surrounding him with other players goes against his philosophy of success

Prime example: Dortmund has no real “star” players, are able to identify quality players before they become great - analytics is seeking to understand playing styles better, and how certain players can fit system of clubs Best way for teams to get better is to improve revenue streaming, attract better players

 Panel #5: Basketball Analytics

Panelists: Steve Kerr, Stan Van Gundy, Brad Stevens, Mike Zarren, moderator Zach Lowe SportVU cameras in every NBA arena now!!

Kerr: interesting to see that for Miami, they are not concerned with their low offensive rebounding numbers because they create turnovers and get more possessions, more efficient than most teams

SVG: you need a style to fit the players/personnel you have, there are different interpretations of pick n’ roll defenses - can’t get caught up in hiring guys that only know analytics and do not know the game of basketball: idea that you can substitute numbers and analytics for actually watching the games - “ I read a useless stat in this ESPN Magazine that said Paul George has ran the most in the league (130 miles). What possible use is that?”

- Stan Van Gundy was by far the most entertaining panelist at the conference! He was in a sense playing "Devil's Advocate" but his arguments were convincing

BS: have noticed some psychological analytics- observe Dirk’s interaction with his teammates, constant talking and communication, always smiling

SK: would be nice to have a measure of conditioning and to be able to measure the stress these players put on their bodies throughout the season

SVG: discussion on the balance between work and rest, many teams now resting their players on some nights or not playing as many minutes “ Michael Jordan when he won his 6 titles never averaged less than 38 minutes/game”

- Tom Thibodeau has success despite injuries because he plays his good guys more minutes than most coaches - why it seems there are more injuries than ever in NBA? Increase in pick n’roll play and strategy, lot more guards attacking rim, very few practices during the season now

“Minute restrictions are BS” - Interesting theory on why Derrick Rose is getting hurt so often: Van Gundy says that guys in league are becoming stronger and more explosive than ever, D.Rose is most explosive in his attacks= larger load on his knees= more injuries Is it the best to be training our players to become stronger and more explosive? Lebron would say yeah

SK: if I am hiring someone in the front office, I want someone with both an analytics and basketball background ideally

Zarren: TANKING solution = the WHEEL(Wheel) – basically picks in perpetuity that would eliminate current lottery system in aims to avoid tanking, submitted it to the league 2 years ago

major criticism: college player that aims at typical team, might stay for another year in college if he knows he’s going somewhere like Milwaukee - could be solved with saying top 3 picks are thrown in a hat, no certainty player knows what team he will end up on or if that pick will be traded

- Another MIT conference staple, Mark Cuban, has provided his idea to avoid tanking, which includes not giving the worst 3 teams in the league picks in the draft, giving an incentive to at least finish 4th worst(Cuban Solution)

Colangelo (Cornell grad!)- Admitted to tanking as Raptors GM a few years ago towards end of season

 This concludes PART 1 of My Recap. PART 2 will cover 2nd Day of Panels.