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

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