“In terms of merit, sports have mathematical statistics. That is how you know who the best player is”. (Norm MacDonald)
Until thirty/forty years ago people would most likely not believe in this statement, but the situation has changed since the end of the 90s when sports started to take statistics more and more into account. Nowadays they are widely used, and every professional team has at least one crew of statisticians on their staff.
Now we apply statistics to every sport, but the Americans were among the first ones to do it and to understand its potential, so here you are going to read about how they applied it in two of the most popular sports in the US.
Some of you might be familiar with the movie “Moneyball”, which is based on a real-life story. Moneyball is a sabermetric approach which finds what is undervalued and then uses it to determine players who cost less than they should. The movie shows the genius of the general manager of the baseball team Oakland Athletics (which competes in the MLB).
It is also important to mention that Baseball is probably the sport which fits best for data analyzing since players tend to play in a structured and repetitive way. This implies two things: we can gather an enormous quantity of data for all different players who repeat the same action again and again, and Baseball has way less variables that can have an impact on the game (unlike other sports such as football which does not have a really structured style of play and hence it has lots of variables that must be taken into account).
When Billy Beane was GM of the Oakland Athletics, the team was covered with debts and had very little money to buy new players, so he came up with the idea to add a team of statisticians in his staff who helped him plan the squad at the beginning of the season. Through a very complicated and long analytical approach the statisticians concluded that parameters such as “percentage of base hits” and “percentage of slugging” which had largely been ignored earlier, were actually incredibly incisive in determining the potential offensive success of a player. By analyzing these parameters that other teams did not consider, they were able to buy underrated players for cheap, and at the same time they sold all those players in their team who had very low rates for these stats.
Beane ended up selling some star players in the roster, and at the beginning many people criticized the GM choices. During the first matches things were not working out well for the team, but by the end of the season the Oakland Athletics had won the American League West and they had been able to break the national record of 19 wins in a row.
The sportive community noticed how useful the Moneyball method was and recognized that there had been a big misinterpretation of the game, so from the next season on all the teams in the MLB started adopting this method as well. Today Moneyball is used in every sport that involves buying\selling players.
Most of the people that have watched at least one NBA game have probably noticed that during the breaks of a game they always reveal statistics such as: % 3 points shots (shots outside the 3-point line), % 2 points shots (shots inside the 3-point line), % free throws, assists and rebounds.
As in the MLB, also in the NBA each team has a group of people who analyze the data of a single player and of the whole roster. Over the years experts came down to the same conclusion, in fact they say that in the game of basketball you can break winning down to two main things: how many points you get per possession and how you get extra possessions. To maximize the data, nowadays NBA teams make sure that their data analytics get as much raw data as possible, so every different squad has its own video-system which tracks down all the movements and all the decisions of players during both games and training. There even exist companies in the US whose task is to develop new technologies to gather basketball data and then sell them to the teams, and this is a constantly growing billion-dollar market in all sports.
Not just data analytics, but also fans have noted that the number of three points shots increases year by year. This is because statisticians found out that in terms of efficiency the mid-range two points shots are worse than three points shots, mainly because three points shots have a higher probability of a favorable rebound for the attacking team and of course they are worth more points. In 1979, when the three-point line was introduced for the first time in the NBA, the average number of 3-point shots per game was 2.8 with a success rate of 28%, while nowadays the mean is 32 shots per game with a success rate of 35.5%. This percentage is continuously growing as well, which is a big turnoff for the die hard fans of the sport, who believe that too many shots make the game boring.
In conclusion, we have seen that data analytics forever changed the way we look at sports, and it plays a more and more important role day by day through new developments. In this article we provided two different sports in which analyzing data is extremely useful.
Last, it is important to state that of course it is impossible to rely only on data when competing since it is very hard to reduce the complexity of human behavior to a mathematical formula. Creativity, craziness, out of the box thinking and talent are hard to quantify. Also, during a single match there could be many factors which could make the difference (weather, fans, injuries), but in the long run data can definitely have a huge impact on an athlete’s performance.