Tuesday, March 8, 2011

Why Sports Analytics?

The "Why" of sports analytics is actually fairly obvious: good sports analytics can help a team win more frequently and more consistently. The real question with sports analytics is not "why?", but "how?" By how I do not mean how do we set up a good sports analytics program - this is a vital question, but not what I am tackling today. Before you determine how to implement a sports analytics program, you first have to understand how sports analytics can help your team win.

Sports analytics can help a team win more frequently and consistently through three general functions: New Information, Synthesis of Information, and Efficient Information. Each team will emphasize a different function of sports analytics based on their core competencies, but a comprehensive sports analytics program will involve all three.
Functions of Sports Analytics
 New Information
The classic use of sports analytics is to provide new information - usually through statistical analysis of performance or game data. This function uses raw, quantitative data to create new information. Team's in all sports have done this privately, creating metrics and projections that help them better measure current and future performance of their players as well as how to best utilize the players they currently have. The power of new information is obvious when compared to the investment community. Investors that have information that other investors do not about the future earning power of a company have a competitive advantage, this why there are laws governing insider trading (hello Martha Stewart).

New Information & the Draft
In sports, if the draft is akin to acquiring an asset in the investment market, than knowing more than about the future potential of the draft eligible players puts your team at a competitive advantage, and more likely to out perform your draft slot - that is you are more likely to draft a player who will perform above the average player selected at that position. One example of this is Chase Budinger and the Houston Rockets. The Rockets are know to have the most advanced analytics program in the NBA and in the 2009 draft they put that program to use. The Rockets traded a future 2nd round pick to the Detroit Pistons for the 44th pick in the draft and selected Chase Budinger. In less than two seasons, Chase has logged over 2700 minutes, hits 35% of his 3s, and has become a regular starter. For an idea on how significantly Chase has out performed the 44th pick, previous players drafted 44th overall include: Reyshawn Terry (yes, that Reyshawn Terry), Tim Pickett, and Lonny Baxter. Houston used their analytic draft system based on college data to provide them with better information on Chase's probable impact on the NBA.

Synthesis of Information
Sports teams, like all organizations, are filled with a many different types of information. There are vasts amounts of quantitative data such as player and team performance metrics, qualitative information such as scouting reports, and hyrbid information such as medical reports. Typically these all reside in various hard drives, spreadsheets, disjoint databases, and word documents. Synergies between the various types of data often exist. That is, putting financial information together with performance data does more than just bring to two types of information together, it creates a new type of information on the value of the player. Good analytic systems bring these various types of information together not just to house them in one place, but to combine them and extract all of the synergistic value out of them.

Efficient Information
Top decision makers with pro sports teams may actually be the busiest people on earth. They are constantly being bombarded with information and requests for their time. Agents, players, fans, owners, and other related parties are always looking to get some time with the top guys and each game, workout, practice, news event etc create more information for them to consider. As Dean Oliver said "stats can see all of the games" which means that decision makers, if they have the time, can consider all of the games. These executives have deep knowledge about their sport, and no one is in a better position to leverage the information that exists into a competitive edge than they are, the limiting factor on them is generally not resources or information, but time. Good information systems give them back some of their time, by creating a more efficient process for them to interact with the information. If an efficient analytic system can easily save a decision maker 10 to 20 minutes a day, that translates into 5 to 10 extra hours a month. That is extra time to spend watching more film, talking through more trade options, exploring additional strategies, and processing even more information. These decision makers will gain a competitive advantage by being able to accomplish more than their adversaries in the same amount of time.

New, better, and efficient information are paths by which a careful investment in sports can yield more wins. It has been estimated for non-sports teams, that an investment in an analytic system with a statistical component is approximately 145% (Davenport & Harris, 2006). While there is no comparable estimate for sports teams, it is not hard to see how analytic systems can help create more wins and keep winning teams on the winning path.

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