Super Bowl Articles

 Probabilities of Square Pool Numbers: 

https://carltonjchin.medium.com/2021-super-bowl-square-pool-odds-by-quarter-fd87c5cbccf0 

"Who Will Win" article

https://carltonjchin.medium.com/who-will-win-the-big-game-super-bowl-lv-2021-d18beae17043 

Enjoy the Big Game!

Who's the GOAT in Men's Olympic Hockey?

With the 2017 NHL playoffs underway and the 2018 Olympic Games coming up in less than a year, we thought we’d take a look at the greatest men’s Olympic Hockey Teams since hockey became an Olympic Sport in 1920. Several years ago, one of us wrote an article on the Miracle on Ice — and used Monte Carlo analyses to study how miraculous the 1980 US gold medal actually was.


Read more here:
http://www.huffingtonpost.com/entry/which-is-the-greatest-olympic-hockey-team-of-all-time_us_58f03401e4b0156697224e1b

Authored by Carlton Chin and Jon Hartley.  Carlton, founder of CARAT / Adamah Capital -- is a graduate of MIT -- and a quant researcher focused on portfolio strategy and sports analytics. He has worked with various sports organizations, including the Sacramento Kings on their Analytics Advisory Council -- and has been quoted by the New York Times, the Wall Street Journal, and ESPN.

Jon Hartley is an economics writer and researcher with interests in finance, macroeconomics and sports analytics. Jon is a regular economics contributor for Forbes and The Huffington Post and has had other writings appear in The New York Times, The Wall Street Journal, Financial Times, and Investor's Business Daily. Jon has also appeared on Fox Business, Fox News, CNN, Bloomberg, and NBC. 

Super Bowl XLVIII Analytics (2014)

As you can tell, we enjoy sports -- and particularly playoffs and championship games.  Here are various sports analytics we have performed for this year's 2014 Super Bowl.  We looked at everything from "Wins Above Average" and Monte Carlo simulations to sports psychology and Pythagorean approaches.

 Sports Analytics approaches

• Monte Carlo sims http://www.rantsports.com/nfl/2014/02/01/picking-the-winner-of-super-bowl-xlviii-with-monte-carlo-analysis/
• Player "Wins Above Average" http://www.rantsports.com/nfl/2014/01/31/2014-super-bowl-wins-above-average-analysis-to-determine-a-winner/

Sports Psychology + Stats approach

http://www.rantsports.com/nfl/2014/01/30/2014-super-bowl-who-will-win-the-big-game/

If you (or your friends) are in those Square Pools -- this could be interesting

http://www.rantsports.com/nfl/2014/01/27/2014-super-bowl-square-pool-probabilities-by-quarter/

Carlton Chin, CFA, holds both undergraduate and graduate degrees from MIT and enjoys applying numbers to everything from sports analytics to the financial markets. He is a fund manager and quant researcher. He has been featured in the NY Times, RantSports, Wall St. Journal,SeekingAlpha & Financial Trader.

Dr. Jay Granat, psychotherapist, named one of America's Top 10 Mental Gurus by Golf Digest, has worked with Olympic athletes & sports organizations. A former university professor, he has authored several books on sport psychology -- and has appeared on ESPN, CBS & Good Morning America.

Article on Monte Carlo and Carlton Chin

We noticed this article on Monte Carlo analysis and our work on sports analytics.

You know something has entered the realms of popular culture when everybody speaks about it in the same breath as Hollywood blockbusters, their tax bills or smartphones. Well, OK… Monte Carlo analysis hasn’t quite made it that far, but it has cropped up in connection with women’s tennis championships, and a number of other sports as well. Carlton J. Chin (portfolio strategist and fund manager when he’s not analyzing sports events) applied Monte Carlo analysis to forecast the results of the 2013 US Open Tennis and the Women’s Singles in particular. So what were his predictions – and, more to the point, was he right?

Image source: tennismindgame.com

The Method Behind the Monte Carlo Madness

Chin asserts that sports are often good candidates for Monte Carlo Analysis because they are marked by specific events: in tennis, such events are, for instance, holding or breaking serve. He used the ability of certain players to hold or break serve drawing on statistics from the rest of the year. Then he used a Monte Carlo analysis in a simulation of thousands of games between these players. His forecasts were that Serena Williams had a 62.3 per cent chance of winning, followed by Victoria Azarenka (16.2 per cent) and Li Na (10.8 per cent). In general, his predictions held good, barring some US Open position upsets like Flavia Pennetta (0.5 per cent) beating her fellow Italian Robert Vinci (6.4 per cent) in the quarter finals.

- See more at: http://decision-analytics-blog.lumina.com/monte-carlo-simulations/monte-carlo-analysis-goes-mainstream-with-womens-tennis/#sthash.ybtt9TZe.dpuf

Quant Facts now 36-23 (61.0%)

Thank you to those who commented how close our Monte Carlo simulations were to the final BCS score.  Our sports psychology factors predicted a very close game -- and selected the underdog, Auburn, in a 35-34 victory.  Florida State, a heavy favorite (about -9 points), stormed back from a 21-3 deficit (and even a 31-27 deficit with one minute remaining in the game) to take the BCS 34-31.

This drops our official quant fact record to 36-23, although the result was definitely more than a moral victory.  Our research shows the importance of factors related to sports psychology.  Our work highlights factors that are under-weighted by sports analysts and sports fans.  Stay-tuned for several upcoming Super Bowl pieces.

2014 BCS: Auburn vs. Florida State

Based on research with my colleague, Dr. Jay Granat - a psychotherapist and founder of StayInTheZone.com - we have analyzed factors related to sports psychology.  Our series of articles is based on several decades worth of championship results (depending on the sport).  Our work has predicted the winner of major sports championships at a 62% rate (sometimes picking underdogs) -- and is regularly featured in the New York Times.   

Here is our analysis for the 2014 BCS game between Auburn and Florida State.  

Defense

Our research shows that defense is more closely related to winning championships than a high-octane offense.  In college football, the team with the better defense in terms of points scored has won 53.3% of championship games since the B.C.S. began fifteen years ago.  Another key statistic has the team with the better defense – as measured by average yards yielded per rush – winning 71.4% of the games over the past 14 years (since the statistic is widely available).

In defensive points against, Florida State (10.7 points against / game) is ranked better than Auburn (24.0).  Florida State also performed better than Auburn in defensive yards per rush.  Florida State held opponents to 3.1 yards / rush versus Auburn’s 4.6 yards / rush.  Edge: Florida State. 

Consistency

Consistency is an important factor in winning championships across all sports.  In both professional football and college football, average yards per rush is a good indicator of consistency and control of the game.  The team with the better rushing statistic has won 57.1% of the championship games over the past 14 years.  Auburn has run for 6.5 yards per rush, versus Florida State’s 5.7 yards per rush.  Edge: Auburn.

Minimizing Errors

Minimizing mistakes is also crucial to winning the big game.  Statistics show that teams which execute better during the regular season also perform well in championships.  In both college and professional football, the team with fewer interceptions during the regular season has won a large majority of title games.

Over the past 15 years of college football, the team with fewer interceptions has won 60% of national championship games.  Auburn threw 7 interceptions this year, while Florida State threw 13.  Although Florida State passes much more than Auburn, Auburn’s interception rate is also better than Florida State. Edge: Auburn.

Big Game Experience & Coaching Leadership

Big game experience and coaching has a positive relationship with winning championships across all major U.S. sports.  On average, these factors work out to about a 60% success rate in sports we studied.  However, the experience factor, as measured by finals appearances over the past three years, posts just a 4-6 (40%) record since the B.C.S. began.  Auburn won the B.C.S. Championship in 2010.  Florida State has not made a championship final game over the past three years.  Big game experience: Auburn.

Florida State coach Jimbo Fisher has a 3-0 record in major bowls, all with Florida State over the past three years.  On the other hand, this is the first major bowl for Auburn coach Gus Malzahn. Coaching edge: Florida State.

Strength of Schedule

Some sports fans will point out differences in strength of schedule, or S.O.S.  For example, Auburn played Alabama (ranked #1 at the time of the game), Missouri (#5), and a total of six teams ranked in the top 25 in compiling a 12-1 record.  Florida State played Clemson (#3 at the time of the game) and four ranked teams during their 13-0 season. 

Over the past 15 years, the team with the better strength of schedule, as measured by SportsReference.com, has won 53.3% of championships.  It is noteworthy that when the difference in S.O.S. has been greater than 3.0 during this 15 year period, the team with the tougher schedule has gone 4-0 in the championship.  This year’s game features the widest S.O.S. spread since the B.C.S. began.  Auburn has a S.O.S. of 6.62 versus Florida State’s -0.06. 

Summary and Football Simulator

Our championship factors related to concepts of sports psychology, favor Auburn 3-2.  In addition, we model football games based on certain random variables, statistics, and our championship factors.  Our football simulator is a probabilistic Monte Carlo model, based on statistics from the regular season. 

Based on our simulator, the final score that comes up most frequently is: Auburn 35 – Florida State 34. 

Carlton Chin, a portfolio strategist and fund manager, and Jay Granat, psychotherapist, are authors of “Who Will Win the Big Game? A Psychological & Mathematical Method.”  They have previously written about the N.C.A.A. men’s basketball tournament, the Super Bowl, and last year’s B.C.S.

2013 US Open Men's Final - Nadal vs. Djokovic

Based on our Monte Carlo simulations, we made a quant fact prediction for the women's champion early in the tournament, that turned out to be correct.  Congratulations to Serena Williams, as well as Victoria Azarenka, who made it a much tougher match than many predicted.  Azarenka is a worthy opponent -- and former number one player in the world, and at age 24, looks like she is poised to regain the top ranking in the world as Serena Williams gets further into her thirties.

On the men's side, our quant fact predictions (based on our Monte Carlo analysis) picks Djokovic in a very close match.  We are looking forward to a great match!  The final is a match-up between:

• ... a red-hot Nadal, who is the favorite based on oddsmakers -- and has been broken only once during the U.S. Open -- against... 
• Djokovic, who has been number one since his fantastic year in 2011 -- and maintains the best return game on hard courts this season.
• In many ways, this is a match between momentum (Nadal has been red-not!) against intermediate-term statistics and rankings (Djokovic maintains the best raw statistics, especially based on his return game).  
• In much of our research, momentum is sometimes overvalued, so our quant fact prediction is on Djokovic.    

We will update the record of our quant fact predictions after the U.S. Open.  

U.S. Open - Men's Quarterfinals

As a follow-up to our Monte Carlo analysis to the women's quarterfinals published in the New York Times yesterday, here is an analysis of the men's quarterfinals.  The research was performed by Carlton Chin, a portfolio strategist and fund manager, and Rose Wang, head of finance at a health care non-profit.  


Monte Carlo Model: Probability of Winning the U.S. Open

1. Novak Djokovic (1) 45.8%
2. Rafael Nadal (2) 25.8%
3. Andy Murray (3) 12.7%
4. David Ferrer (4) 6.6%
5. Richard Gasquet (8) 5.0%
6. Tommy Robredo (19) 1.9%
7. Stanislas Wawrinka (9) 1.8%
8. Mikhail Youzhny (21) 0.5%

The Monte Carlo model gives Novak Djokovic an edge over Rafael Nadal and Andy Murray. In addition, Djokovic has the easiest quarterfinal matchup of the three top seeds, at least statistically.


Read more here:
http://straightsets.blogs.nytimes.com/2013/09/04/keeping-score-monte-carlo-analysis-of-mens-draw/?_r=0

The U.S. Open (Women's Quarterfinals) and Monte Carlo Simulations

Below is an excerpt of quantitative analysis performed for this year's tennis U.S. Open, picked up by the New York Times.   The piece is entitled, "Using the Monte Carlo Method in Tennis" and is by Carlton Chin, a portfolio strategist and fund manager, and Rose Wang, head of finance for a health care non-profit.  

A Monte Carlo analysis uses a random process to assess a complicated problem. In the financial world, Monte Carlo methods can help study the risk of investment strategies or to evaluate derivatives. Sporting events can also show the power of Monte Carlo simulations because sports can be broken down into specific events like an at-bat in baseball, or a possession in football or basketball.

Tennis can be deconstructed into actions like holding serve and breaking serve. The game can be broken down further by analyzing statistics like first-serve percentage and the percentage of points won on first serve or second serve.

***
1. Serena Williams (1) 62.3%
2. Victoria Azarenka (2) 16.2%
3. Li Na (5) 10.8%
4. Roberta Vinci (10) 6.4%
5. Ana Ivanovic (13) 1.6%
6. Ekaterina Makarova (24) 1.2%
7. Carla Suarez Navarro (18) 0.8%
8. Flavia Pennetta 0.5%
9. Daniela Hantuchova 0.2%


The piece has Serena Williams as a favorite to win another Grand Slam, and is an official quant fact prediction for the book's blog.

Read more here:
http://straightsets.blogs.nytimes.com/2013/09/03/keeping-score-using-the-monte-carlo-method-in-tennis/?_r=0#more-34506

Super Bowl Sims and Championship Characteristics - 2013

Here is an excerpt from our analysis and numerical simulations of this year's Super Bowl, between San Francisco and Baltimore, which is published in the New York Times.  Our quant facts have been correct more than 60% of the time.

Consistency: Ball control remains one of the more important offensive indicators studied in both professional and college football. The team with a better running game, as measured by average yards per rush, has won 57.8 percent of the Super Bowls. The 49ers averaged 5.1 yards per rush this season, compared to 4.3 for the Ravens. Edge: San Francisco.

Monte Carlo Simulations: Probabilistic models like Monte Carlo techniques can be used to solve complicated problems. Similar to our analysis for the B.C.S. national title game between Alabama and Notre Dame, we used regular-season statistics in combination with our championship factors to simulate thousands of football games between San Francisco and Baltimore.

Read more here:
http://fifthdown.blogs.nytimes.com/2013/02/02/keeping-score-what-stats-and-simulations-say-about-the-super-bowl/ 

In addition, our popular analysis of Super Bowl Square Odds -- by Quarter -- is published here:
http://ireport.cnn.com/docs/DOC-917541

College Football's 2013 BCS: Alabama vs. Notre Dame

Here is an excerpt from our "Who Will Win?" analysis on the BCS game between Alabama and Notre Dame, that was published by the NY Times.  


We typically apply a quantitative analysis of sports psychology concepts to predict the winner of various sports championships.  In this article, we took this a step further and used quant investment techniques (including probabilistic Monte Carlo methods) to develop a football simulator to take a look at the big game between Alabama and Notre Dame in this year's college football national championship BCS game.

Here is an excerpt from the article in the NY Times.

... our work relates key statistics to sports psychology concepts like leadership, consistency and minimizing errors. We have also developed a football simulator based on a probabilistic Monte Carlo model to use in conjunction with our championship factors. 

Simulation: We developed a football simulator that plays out thousands of games relatively quickly. The simulator is a probabilistic Monte Carlo model that uses statistics and results from the regular season.

... football games can be modeled based on certain random variables, statistics and our championship factors. The final score that came up most frequently on our simulator was Alabama 27, Notre Dame 14. As expected, we got a cloud of widely varying results, but this is the center of the model's probability distribution.

Edge: Alabama

Read more here:

http://thequad.blogs.nytimes.com/2013/01/07/keeping-score-characteristics-of-a-champion/#more-44985

Carlton J. Chin, a portfolio strategist and fund manager, and Jay P. Granat, psychotherapist, are authors of "Who Will Win the Big Game? A Psychological & Mathematical Method." They have previously written about the N.C.A.A. men's basketball tournament, the N.B.A. finals and the Super Bowl.

Olympics: Revisiting a Popular Article

With the 2012 Olympics set to start in London (and today being July 4th), we thought we would revisit one of our more popular articles -- on the 1980 Miracle on Ice.  In that article, we looked at concepts of sports psychology and did an interesting and fun analysis (including Monte Carlo simulations) to study the odds of the U.S. ice hockey team winning the gold medal.

Herb Brooks was a true master of sports psychology, graduating from college with a degree in psychology.  Brooks knew he had a chance to make some noise at the Olympics - especially with the US hosting the Winter Olympics.  Brooks worked at gathering players who could lift their games to special levels -- and could also play as part of a team where the sum of the parts were greater than the individual pieces.  

On goaltender Jim Craig and other players, Brooks said, "I don't want the best players, I want the right players."  Brooks knew what he was doing when he put his 1980 team together, piece by piece.  He also knew how to "push the players' buttons" and was tough on many members of that Miracle on Ice -- but eventually created one of the most beloved U.S. sports stories of the twentieth century.

Based on key hockey statistics and expectations, we performed a Monte Carlo simulation to study the odds of the United States hockey team winning the gold medal. Monte Carlo methods use a random process to solve complicated problems...

In a similar manner, hockey games can be modeled based on certain random variables and key statistics, including shots on goal, save percentage and shot efficiency. If we model the United States team as a seventh seed, the probability of the United States winning the gold medal approaches odds as high as 1 in 1,000. 

But the United States turned out to be a stronger team than expected. Entering the medal round of the Olympics, the United States and Soviet teams were undefeated. The United States was 4-0-1, outscoring their opponents by 25-10, while the Russians were 5-0, outscoring their opponents by 51-11. The Soviet goaltenders, Vladislav Tretiak and Vladimir Myshkin, averaged a solid 88.2 save percentage, and the American goalie Jim Craig had a 91.7 save percentage.

If we base our simulations on the team’s performance in the tournament, the odds could have been as low as 17-to-1 for the United States win the gold medal.

The entire article was originally published in the NY Times in 2010, on the 30th anniversary of the Miracle on Ice.  Read more here:
http://vancouver2010.blogs.nytimes.com/2010/02/22/how-miraculous-was-the-miracle/?scp=1&sq=miraculous%20miracle&st=cse