Analyzing The Growth Of Analytics In Sports
The growth of numerical analysis in sports is simply mesmerizing. Teams have been able to use the concepts of sports analytics to better strategize and make educated decisions on players they should add in their sport’s respective drafts and free agent periods. It’s not enough to know a player’s “point per game” output anymore; the growth of sports analytics has brought forward complex numerical variables to measure the advanced performance of players in all sports. Analytics can show us a variety of important elements of a player’s/team’s production or abilities, but at the same time there are some factors which cannot be defined by data. Analytics is what has driven some teams to their peak potential, which is why its growth is so rapid.
What Can Analytics Tell Us?
Basic Production
In terms of measuring a player’s basic production, analytics are definitely able to provide accurate information on the output of the player. As mentioned earlier, “points per game” or “number of yards” is simply not enough to show us the full profile of a player’s performance. These numbers are vague and do not tell the full story; the total amount of a player’s output in a certain statistical category can always be impacted by his attempts and efficiency. Meaning: a basketball player who scores 22 points while making 11/14 shots produced better and more efficiently than a player who scored 30 points on 15/28 shots. The importance of production efficiency has never been underestimated, but now is when statistics are finally showing a players efficiency of production.
Analyzing Growth
While basic production can be clearly shown through sports analytics, analyzing a player’s growth is indefinite. Growth is an aspect that varies from player to player: while one player’s numerical rise in a certain statistic can mirror that of a perfect line graph, other players can have their year by year increase scattered all over the place. While being able to compute functions on a player’s growth is possible, there are many factors (injuries, teammates, dynamics, etc) that can help/harm a players growth or production. Thus, while growth can be potentially modeled by analytics, it is much harder than basic production because of the external factors that affect a player’s growth.
Immeasurable Aspects
There are some aspects of sports which have profound impacts on a player’s and team’s performance, but cannot/have not been measured and/or defined with numbers. These include game impact, game IQ, drive, will to win, work ethic, etc. While game impact and IQ can be potentially be measured/defined in the future, the others purely center around the qualitative analysis of a player opposed to the quantitative one.
How Have Analytics Helped Teams?
Oakland Athletics (Baseball)
In 2001, the Oakland A’s finished with a record of 102–60. They finished 2nd seed in the AL West, and showed promise for a great future. However, after the season, their top 3 players all joined new teams. And Oakland, being a low-profile team at the time, didn’t possess the financial strength and abilities to replace their core trio with expensive players on the market. So what did they do? They turned to data. A’s GM Billy Beane turned to one of the A’s scouts, Paul DePodesta, who was a Harvard graduate with a degree in economics. Beane and DePodesta mined through data, and found that it was the undervalued statistics — ones like on-base percentage and slugging — which had the greatest correlation with winning baseball games. They complied a list of players who didn’t look the best on paper, but whose statistics were strong in the areas which Beane and DePodesta found to have the greatest impact on team. Despite reluctance from the rest of the organization, Beane signed these players that were on his list, and the moves paid dividends. That year, the A’s set the AL record for most won games in a row (with 20), and finished with an overall record 103–59, which was tied for 1st in the MLB. Who were the A’s tied with? The New York Yankees, a team with $120 million in salary. The A’s were able attain the same record with just $40 million in salary space, three times less. They were able to do this thanks to the power of analytics.
Houston Rockets (Basketball)
The Rockets have based their team intel off of big data and analytics. This numerical based approach stems from their general manager, Daryl Morey. Unlike most basketball GMs, Morey did not have a professional basketball career. He received a computer science degree from Northwestern and an MBA from MIT before numerous sports analytics jobs and working his way up the Rockets front office. The team’s strategy is centered around maximizing the number of 3-point shots the team took. Morey, someone very skilled in the data science field, used analytics to determine the best players to comprise of this sharpshooting team. Instead of going through just traditional stats like 3-point percentage or 3-pointers per game, Morey sifted through advance metrics to develop a roster which would achieve great success in the NBA from the 3-point shot.
What’s Next?
The use of data in sports is a phenomenon that is on the rise. So far the makeup and intel of teams has been impacted and somewhat determined by big data. As time has gone on, however more aspects of sports have been able to apply analytics. An economics-based approach towards draft picks is something that has the potential to revolutionize the way teams select college players. Basing team strategy, not just the players executing it, off of analytics is a methodology which is starting to take it’s shape. Lastly, it is possible that we will see the growth of advanced metrics in sports. More metrics will mean that numbers can define more aspects of team’s/player’s output or performance.
All in all, the growth of analytics has been a factor which has had a tremendous impact on many teams. It will exciting to see the journey of this concept in the coming years.
