Bill Beane on Evaluation of Players
Bill Bean introduced sabermetrics in baseball, an approach that used data to capture players' performance, like hits, catchers and getting bases. He incorporated data from the industry, software developers, and the New York Exchange, over a long time, and generated relationships that guided his approach to signing players. He did not consider speed and strength (though he did not decant them), rather, he was looking at the qualities that made the player different. All major scouts went around the country, looking at high school players and stars, but Bill had noticed that players assigned straight out of high school did not develop into full-blown stars. He started recruiting from colleges, mostly looking at players who were best at specific moves, like catching or forcing opponents to pitch. He also looked at the data for players in the league to determine underused players who he could buy cheaply or get for free.
Financial impact
Oakland A’s, a relatively small team without the resources like those of the New York Yankees, improved consistently for the four years that Bill Beane was in charge. In the 2002 season, for instance, Oakland A's paid players $44 million compared to Yankees $125 million payrolls, but still managed to win the league with a record 103-59. Bill Beane's strategies ensured that, to compete with the best teams, he had to use the little resources he had to buy the best players, so he decided to buy undervalued players. The idea helped in that he could get superb players at a small fee or for nothing in the case of Scott Hatteberg, who had been released by Boston Red Sox after a nerve injury. Scott was the best player at getting on base but Red Sox viewed him as a catcher.
The revenues of Oakland A’s shot up as a result of the new success, peaking when the team won twenty games in a row in the 2002 season. There is evidence that the team still uses the approach of Bill Beane, considering that the total salaries in the 2016 season total to $87 million compared to Los Angeles Dodgers whose payroll is $245 million. However, the fortunes on the field have dived, for Oakland Athletics, arguably, because the use of data became uniformly spread in baseball, and stopped being a source of competitive advantage for Oakland.
Use of statistics in baseball
Despite the success of Oakland A's in 2002, and the years preceding, use of statistics has not completely been incorporated in baseball. Without a doubt, there is more data available on players and teams, and sabermetrics has even become a branch of statistics. However, failure of teams to invest in creating an end-to-end process that trains everyone how to use data has made it difficult for statistics have an across the board effect. Some teams use the traditional methods of scouting; they sign players on the basis of physical prowess, power, and speed, making teams that have great strengths and big weaknesses. This gap can be blamed for the dismal performances of some of the teams with high payrolls.
Cleveland Browns on Hiring Depodesta
Cleveland aims to use the skills of Paul Depodesta to introduce analytics to football in the National Football League. Depodesta proposed a need to make decisions using data, to a scale of 60%. Some of the numbers he will be keen to look at include call plays, practice time and weighing trades. Depodesta will also be keen on using the RFID (Radio frequency identification) signals in making analytical inferences about opponent performance (Fleming). The data signals are collected through badges worn by players on the pitch, and such aspects of play like speed, positioning, and movement on the pitch are measured. Paul Depodesta will use his experience in baseball to make a data toolkit for NFL, going by his experience and groundbreaking success he had with Oakland A’s.
Lessons from analyzing Moneyball
Statistics are fundamental in the modern sporting world and more. For once, analytics help decision makers to overcome biases and ideological inclinations in the favor of hard data, and make decisions that disrupt industry norms. For Oakland A's, for example, Bill Beane was able to use sabermetrics to scout, select and buy players, bringing into the team the kind of balance that could not have been purchased with money. The successful implementation of the system makes it independent of any player because the setup determines roles. That is the reason why Oakland A's won so many games despite losing three of their best players to competitors. In business, analytics can be used to measure trends and customer preferences. Moneyball and the success of the Oakland is a testimony of the relevance of statistics in the modern world.
Value – Insight
Works Cited
Fleming, David. Will Paul DePodesta and Moneyball work in Cleveland? ESPN.com, 11 Apr. 2016. Web. 30 May 2016.
