Analytics In Basketball

Baseball has always been a game of numbers. Fans of the game have grown up being able to recite them by heart; Ted Williams’.406 batting average, Joe DiMaggio’s 56 game hitting streak, Babe Ruth’s 714 home runs. These numbers hold a special place in the history of the game. Statistics such as batting average, wins, home runs, and runs batted in have always been there to tell us who the best players are. Your favorite player has a .300 batting average? He’s an all-star. He hit 40 home runs and batted in 120 runs? That’s a Most Valuable Player Award candidate. Your favorite team’s best pitcher won 20 games? He’s a Cy Young Award contender. These statistics have been used to evaluate player performance

The book Moneyball by Michael Lewis is about a former major league baseball player who became the manager of the Oakland A’s. It tells the story of how he led the team to success despite their low budget by using computer based analytics to draft players. With the help of Bill James, the Oakland A’s came up with a new plan based on statistics to draft players. He went after players nobody wanted due to their low budget and his new plan. Billy led the Oakland Athletics to a successive win seasons by changing the way he measured players. He abandoned the traditional 5 “tool” the other scouts used and adopted empirical analytics. The abandonment of the traditional assessment of

MLB teams are finding new innovative ways to use analytics, one way is to use the statistics for a more effective way to evaluate free agents. “Astros employed an analysis based on the TrackMan system to acquire an unaccomplished pitcher called Collin McHugh, because of his fast-spinning curveball” (“Every Step They”). “They then told him to throw that pitch far more often during the next season, and he blossomed into a star” (“Every Step They”). General Managers and coaches can use these analytics to scout areas they have never scouted before. Analytics gives teams more resources and more assistance to evaluate players. “For example, by comparing the number of strikes called for a catcher in relation to every other catcher in the league, the data can illustrate how good any one catcher is at getting umpires to call strikes” (Nadler). “Additionally, general managers and scouts can use

By looking at the frequency histogram comparing height and amount of NBA players it is noticeable that the most NBA players are 6’3” to 6’8” and that there is very little NBA players in the data that are 5’9” to 6’2”. In the other histogram, comparing free throw percentages and amount of NBA players almost all of the NBA players in the data make 75% to 89.9% of their free throws and very little make make under 75% or over 89.9% of their free throws. These histograms show that most NBA players, with all different heights, make almost the same amount of free throws as other NBA players.

Statistics, specifically how they measure and control data, as well as help us learn from it, has and always will be a part of baseball (“What is statistics”). Stats are very, very useful for many purposes in the sport, and can help keep some analysis simplistic. The most effective use of stats in baseball is the comparison between teammates within the same season. Within these parameters, the numbers come from very similar environments, as the teammates are playing against the same teams in the same ballparks at the same time. There are no outside factors potentially skewing the

The study of sports analytics is a common strategy and performance tool for players and fans. Many professional athletes will study analytics to help improve their game. Not only do professional athletes use this to their advantage but fans and workers in the sports world can use it to study the game and make wise predictions. Sports analytics also provides an in-depth evaluation of how numbers can track a particular sport and helps us follow along to a closer degree. Sports analytics provides a strategy for studying player’s habits and the selected sport as well as digging deep into the root of the game and understanding it from an analytic point of view.

Thesis Statement: The trend that makes three-point shots are more prevalent in the NBA have a positive effect on basketball players (professional and amateur); since shooting a three-point shot does not require height compared to dunking, there is a higher chance that guards can be involved within the game offensively and defensively. Alphanumeric Outline: The Incentive of Shooting a Three-point shot I. Introduction A. NBA champions in past five years 1. All teams are great three-point shooting teams B. Increase in the number of three-point shots attempted in the NBA 1. The statistic shows the difference of past few decades C. Thesis statement: same as above II.

Using these numbers, they can then create several mathematical models that then are used for statistical comparison of each of the players. Football uses statistics of everything like how many passes or catches a player makes. Then some of the coaches of the different teams use these statistics to compare each of the achievements the player makes. Realizing that there is the use of mathematics in everything like a simple and fun football game, shows me that you can calculate a lot of things and get statistics from them, these can help me in my math course and during my life because the use of statistics and the information I can get can make me know the probabilities of things happening or like the coaches in the teams that use them to compare things, I can use them for that too. I should use statistics more in my life when they can apply because they can make a difference in how I see things and make my life easier if I know what are the probabilities of an event happening, even if I am trying to choose between several things, I can get statistics to see which one is better or which one is the best option

Stats are continuing to grow in prevalence in all sports as a partial result of Moneyball and are seen as a valuable resource. There are always new innovators coming along increasing the prevalence and ability that statistics have. An example of this is Wenger an innovator in using statistics in soccer “A pioneer in using data analysis to monitor and improve performance, he ended the culture of heavy drinking at Arsenal and introduced dietary controls and a strict fitness regime”(Kucharski). Wenger is the

Analytical data includes different psychological aspects of a player’s mentality. Stopwatches record times, tape measures calculate distances and heights, plus video gives scouts and coaches a visual look at player performance. However, for all their usefulness, they do little more than categorize athletes based on a series of numbers.

A potential issue arises from talent identification as it is not an exact science and often subjective, and with research showing that physical and biological attributes alone do not have great effect on player skill in team ball sports (Burgess., 2010). The use of these should be limited or only used as a compliment to the broader decision-making process. However talent identification programs are heavily influenced by these measures (Abbott.,

The ACC believes that their conference and its respective universities are equipped with the best analytics software at their disposal. With the use of Sport Source Analytics, the ACC conference has managed to gain advantage at predicting outcomes within the conference matchups as well as outside of the conference. Having the opportunity to use this analytics software to predict match-ups also allows the conference to improve individual team/player performance. Ultimately the usage of Sport Source Analytics will allow the conference to continue to improve sports performance which can produce a larger fan base for the team; this will eventually lead to the conference making more

From reading the excerpt Golfers Join The Rest of World, Use Data, it automatically caught my attention how much sports analysis is spreading around the sports world, including golf. Although personally I am not familiar at all with the world of golf, it wasn’t hard to understand from reading the first sentences on how a player who had limited playing experience at the National Augusta National Golf Club and who showed up late to practice was able to win the Masters by using analytics. That player being Danny Willet.

Thesis: This paper argues that basketball is the most difficult sport, for a number of reasons that will be pointed out through the literature on the game, the coaches, and the players.

Sabermetrics is a systematic statistical approach in evaluating teams and players. Based on this science it was found out that this basis for judging the performance of the player should be on-base percentage. The Oakland A’s recruitment would prioritize college players rather than high school players. This was due to the fact that college players have already played more games against better competition. Beane was convinced with the fact that “a young player” is not what he looks like, or what he might become, but what he has done. The bottom line is what the player has produced in college. Beane and DePodesta believed that they could forecast future performance of college players more efficiently than high school ones.