Richard Coleman

**Richard Coleman****CEO of Hockey analytics-NHL**Richard Coleman is a statistician from Stamford, Connecticut, who has revolutionized how teams recruit players and build their rosters. In 2005 he presented his insights to the NHL's General Managers, ultimately leading five teams to use his analytics services. His groundbreaking metrics have made a lasting impact on the industry - especially when evaluating potential players - as evidenced by Corsi, which evaluates attempts made on the goal rather than merely counting shots that get past the goalie. Before starting Coleman Analytics, Richard gained experience working at Harvard University Medical School in Boston and Stanford University Medical School in California. He partnered with Mike Smith, former general manager of the Chicago Blackhawks hockey team, to design a system for providing relevant data and actionable information to improve team performances.

Coleman's software programming is an intricate calculation that can trace player and team performance more accurately. With this software, NHL teams can gain an edge during games thanks to greater detail collected from breaking it down into multiple layers. This stands in contrast to conventional methods where data was limited due to simply counting shots that got past goalies. As such, Coleman has become an expert on hockey analytics, and his influence has been felt across hockey and many other sports within professional leagues. Richard Coleman is a testament to how far one can go through dedication and focus on their chosen field of expertise. His passion for statistics inspires others who want to leave their mark by building something significant out of data analysis and those still exploring their individual paths.

**SCHEDULING & SELECTING RELIEF PITCHERS****TO WIN ADDITIONAL GAMES****SYNOPSIS:**Richard Coleman Chicago Blackhawks used

**Artificial Intelligence**to help select NHL draftees Preliminary Artificial Intelligence Methods for Predicting NHL Draftee Outcomes.- Softmax Regression

The softmax function, may at first appear similar to a one vs. all logistic regression classifier, as it leverages multiple logistic regressions and maximizes over a set of outcomes. However, in the one vs. all logistic regression model each regression is optimized separately, and therefore, it is difficult to compare the outputs of each sub-model. In contrast, the softmax function produces a set of implied probabilities, one for each of the four classes:2

**exT wj****P****(****y****=****j|x, {wk }k∈{****1****,****2****,****3****,****4****}****) = 4****k****=1****exT wk**Thus, in our case, the softmax function produces a much more interpretable outcome, which allows us to build an under- standing of where different players lie between the different classifications. For instance, a player may not conclusively rank as a 3 or 4, and the softmax output will reflect this lack of certainty.

For the sake of classification, we leverage the softmax regression, also known as a multinomial logistic regression, which determines a player’s classification based off the maximum likelihood from the softmax function. Similar to the one vs. all logistic regression classifier, we trained our softmax regression on the 505 instances of our training set, and then applied our model to the 169 instances of our test set (See Figure 4).

**Softmax Regression Classifier: Train Outcomes Softmax Regression Classifier: Test Outcomes****1 1****2 2****3 3****4 4****1 2 3 4****Predicted label****1 2 3 4****Predicted label**Figure 6: Softmax regression classifier confusion matrices for the training and test sets

Given the confusion matrices above, our softmax regression classifier performs with 59.8% accuracy on our training set and 53.8% accuracy on our test set. From this, we can see our softmax regression performs very similarly to our one vs. all logistic regression classifier, and this holds because both of the models are generalized linear models.

Therefore, a generalized linear model may not be the best fit given our data. We also have a small training set, about 500 rows, given our number of continuous features. The lack of data may further hinder the performance of our models, but this can be alleviated by clever feature selection and possible data augmentation (See Section 5).

- Naive Bayes

Although we usually see Naive Bayes applied to binary classification, we can use the same Naive Bayes approach to solve multiclass classification problems. In contrast to the previous two models, Naive Bayes is a generative model, as opposed to a generalized linear model. Given an input vector x = (

*x*0*, ..., x*10), which again in our case represents an AHL player’s data, the Naive Bayes model assigns conditional probabilities*p*(*Ck|x*0*, ..., x*10) for each of the*k ∈ {*1*,*2*,*3*,*4*}*, possible classes.Richard Coleman Blackhawks descrived, In order to avoid permutation issues,

*p*(*Ck x*0*, ..., x*10) is reformulated to*P*(*Ck, x*0*, ..., x*10) The model then chooses the ranking with the highest conditional probability given the available data. The main assumption that the classifier makes is that the different features are independent of each other, which is not always true in practice but can provide a good approximation of the true distribution.3Similar to the previous models, we trained our

**Naive Bayes classier**on 505 instances from our training set, and then applied our model to 169 instances from our test set (See Figure 7).**Naive Bayes Classifier: Train Outcomes Naive Bayes Classifier: Test Outcomes****1 1****2 2****3 3****4 4****1 2 3 4****Predicted label****1 2 3 4****Predicted label****How Richard Coleman became a leader in hockey analytics (part 3)**How I got into the NHL

In the summers, I, Richard Coleman lived in Chilmark on Martha's Vineyard. I had a boat, and on Sundays, I typically would take my friend Eddie along for a boat ride or fishing. One Sunday in July, he brought along an 11-year-old boy who was a camper at the

**summer day camp**Eddie worked with. About an hour into our boat trip, Eddie told me he had to return to the center to teach a tennis lesson. I drove him near the shore, and he jumped into Vineyard Sound and swam to the beach. Suddenly I was left with this 11-year-old boy. I let him steer the boat and do some fishing, and then I told him he better calls his parents and let them know where he was. He got on the boat phone and said something like: "Dad, this is so cool. I'm driving this boat with Richard Coleman and will be home in an hour. The." message was so cute that the parents saved it on their message machine for that summer. When the ride ended, I drove the boy home and dropped him off.Next month, in August, at an outdoor barbecue party, a man came up to me and introduced himself to me as Jay Grossman, the father of the 11-year-old boy. He thanked me for the boating experience with his son. He mentioned that he was an agent for NHL hockey players, and I told him about my baseball work with the Red Sox. He asked me if I could help him with a project. He was negotiating a contract for one of his players Ilya Kovalchuk with Don Waddell, the General Manager of the Atlanta Thrashers. The GM was using Marian Hossa as a comparison player, trying to show that Kovalchuk was a lesser player and thus deserved less of a contract. Over the next few weeks, I analyzed the statistics used by both sides for the salary negotiations and came up with some interesting findings and suggestions. After that, Jay mentioned he knew Mike Smith, the recently fired general manager of the Chicago Blackhawks, and that Mike lived on Martha Vineyard and that he could set up a meeting for me.

I drove up in my 1957 Chevy Bel Air to Mike's furniture shop he was running on the island and introduced myself. He was very interested in the car, and then we sat down, and I told him about my baseball work. He said if you could do something with high pressure for hockey analytics, that would interest NHL teams. I thanked him and decided to try something for hockey. Working with my programmer, we devised something simple but revolutionary at that time: net shots. For many years NHL teams tracked plus/minus statistics. Each player got credit for being on the ice when their team scored a goal and a demerit for being on the ice when the opponent scored. Since dreams are relatively rare in the NHL, about 5 per game, the plus-minus statistic is somewhat limited in

**differentiating player talent**. Our new measure was net shots. Each player got credit for being on the ice for a team picture and a demerit for a team photo. Since there were about 60 shots in a game, the database was much more extensive and meaningful. We also used a mathematical correction factor to adjust each player's results based on his team's overall net shot rating; we also measured each player's impact on his teammate's net shots. Furthermore, we measured usage and performance during high pressure.

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