The Superstar Effect in Economic of Sports, Book Review Example

Berri’s (1999) article attempts to scientifically calculate each basketball player’s contribution towards a season, in order to determine their real value for the team. The author (Berri, 1999, p. 412) argues that “it is not clear whether the players are evaluated according to their on-court contribution, or their off court image”. The question is interesting for two different reasons. First, because individual perceptions of a player’s performance can never be unbiased and objective. Second, assigning a standard value to each action on court can be a revolutionary idea, however, it does not differentiate between situations. As an example: an offensive rebound can result in a basket, or the player can lose the ball two seconds later, and in that case it has little or no value. While scientifically creating averages for each action is a good starting point, Berri’s (1999) research is still standardized.

The author argues that it is possible to determine the impact of each factor in wins, and – based on individual player statistics – assign a value for each member’s contribution. There is one interesting point that the author makes, and this is related to court time and opponent performance. Using these crucial variables, is is important to create a value that is somewhat – even if not completely  – adjusted to the situation. Taking a per minute production as a standard measure, the author creates a framework that measures time-adjusted productivity.

The three step model described by Berri (1999) is simple to use, and so far the most reliable statistical method for measuring on court performance of basketball players. In the first step, statistics of individual players are measured and analyzed. Step two examines the team statistics, and in step three, the relative value of the player in the team.

Analyzing the data of regular seasons, the author found that it was indeed not Jordan who should have been named the most valuable performer, but Malone. In seasons 1997-1998, however, Dennis Rodman came out as a winner. As Rodman was the best “rebounder” in the team, and rebounds were assigned a high productive value, he was able to score higher than other players who remained in one position. The author, however, went further than creating a statistical table, based on the formula created; he tested the methods as well. Overall, the productivity measurement blueprint was found to be reliable.

It is also important to note that different results were recorded for the entire season and finals. If we consider that finals’ results generally matter more, it is possible to assume that players who perform best during the finals are the most valuable. Still, it is important to note that basketball is a team game, and there are several factors that impact one’s performance. Knowing the drill in and out would never make a person the most valuable basketball player; being able to work with others under pressure, making the right decisions on time, and listening to signals of the opponent team and fellow players also counts. Individual player styles can also not be considered when doing statistical analysis, and it should not be the aim of the research, either. Some players would score more points by utilizing their rebound abilities, others are excellent at 3-pointers, and make up for their shortcomings in court mobility and versatility.

It is possible that the decision to name Michael Jordan the most valuable player was based on sports experts’ individual perception on his game style; teamwork, persistence, agility, and speed. While there will never be an agreement about who is the best basketball player of a season, and all fans would have their favorites, when attempting to determine an individual’s value and performance, the statistical analysis framework created by Berri (1999) can be extremely useful.

References

Berri, D. (1999) Who is ‘most valuable’? Measuring the player’s production of wins in the National Basketball Association. Managerial and Decision Economics. 20: 411–427 (1999)