Free Throws in the NBA
By: Samuel Flusche, Sherry Hancock, Eric Vandament
Free Throws are possibly one of the most uneventful plays within a game of basketball, they are also one of the easiest shots. Despite this they are still crucial for the game. To gain a better understanding of these easy but important plays we performed an analysis of free throws.
To do this analysis, we used a dataset regarding Free Throws from NBA Stats from API with the missing data manually filled in by Basketball Reference. The data set ranges from the years 2006 to 2015 and was roughly 100 MB in size. Within this dataset, there were 598,151 rows and 32 columns. Some of the columns that we focused on were shot made, shot count, player, season start, win or loss, playoffs, and period.
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The first main area of focus for our analysis was players. Within our dataset, there are 1,039 different players who attempted a free throw. Naturally, we wanted to determine who was the best free throw shooter within our entire dataset. Initially, we summed up the points made by players to get the following table.
Looking at this table one might believe that LeBron James is the best at free throws, however, this doesn’t consider the number of free throws attempted. This is important because Lebron has also attempted the freest throws within the dataset. To account for this, a new column was created that will be named ‘shooting percentage’. This column takes the sum of the points made per player and divides it by the number of free throws they have taken, thus giving us a percentage of how many free throws they make. After filtering out players who have taken less than 500 shots we see a different story.
With this new leaderboard, you can see that Steve Nash made 91.39% of his free throws, which is much higher than Lebron’s 77.45% shooting percentage.
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Our team wanted to look into shot count. This column is describing what shot on the free throw line is the player taking. Grouping this column with shots made, you can see if the player made their shot on each shot count. We focused on two groups. The players with the most amount of points are, Top Scorers. Also, the players that have a higher shooting percentage, Top Shooters. As you can see both groups are more likely to make their second shot than their first shot. In basketball, it is rare to have a third free throw shot. As anticipated, Top Shooters are more likely to make their third shot.
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Since our dataset ranges from 2006 to 2015 we thought it would be interesting to look into how free throws have changed over the years. One common point of view on the NBA today is that it has become soft and that everything is called a foul. Due to this, one would believe that the amount of free throws per year has gone up. However, in fact, they have done the opposite.
Looking at the graph, we have the x-axis with the years on it and the number of free throws taken on the y-axis. As you can see the amount of free throws per year has been trending down, going from 65,950 in 2006 to 59,291 in 2015. This may still be due to the fact that the NBA is getting soft. One possible reason is that players are more scared to foul others now since they know refs are more likely to call it resulting in fewer fouls and fewer free throws. (2011 there were significantly fewer free throws attempted due to fewer games because of a lockout). Even more interesting is that despite this decrease in free throws over the years. The shooting percentage as a league per year has bounced around between 75% to 77% showing no real trend.
After finding that throughout the seasons there was a trend of free throws decreasing we wanted to look into the individual players. We grouped up our Top Scorers and Top Shooters to see their free throw percentages. We found that Top Scorers are more consistent with their shooting percentage .
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One hypothesis that we had was that the shooting percentage would go down in games during the Playoffs. The reasoning for this is the pressure, players must deal with the nose from a packed stadium and millions of fans watching them. To find this out we looked at the playoff column within our dataset. Games that are during the regular season are represented with a 0, and games during the playoffs are represented with a 1. Knowing this we can create a table showing us the number of shots made, shots taken (shot count), and the shooting percentage.
After creating this table we found that our hypothesis was incorrect, with less than a 0.5% drop in shooting percentage from the regular season to the playoffs. This is understandable once you consider that the teams in the playoffs are better than most other teams that year. Usually having better players with better individual free throw shooting percentages.
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After looking into playoffs and finding nothing we wanted to break down free throws by period to see if there were any differences. During a regulation game, there are 4 periods and if games are tied they go into a 5 minute overtime period. These overtime periods will continue till a winner is determined. Within our dataset, the largest overtime period is period 8 or 4th overtime. Due to the number of games going into overtime being so low, we have grouped all overtime into one called period 5. After making these adjustments, we graphed the shooting percentage of free throws by period. See the graph below.
Looking at this graph you can see how the shooting percentage stays pretty consistent during the regular periods. However, during overtime shooting percentage increases by over 2%. This could be due to the fact that some players intentionally try to get fouled because they know they are good at shooting free throws. To build off this, despite the shooting percentage going down from the 3rd period to the 4th period. The amount of free throw attempts increased from around 150,000 free throws attempted in the 3rd period to around 180,000 in the 4th period. This is more than because of strategy. When a team is losing, they will wait until a player with a bad free throw shooting percentage gets the ball and will foul them, hoping they will miss the shots and get the ball back.
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The next big question our dataset offered us was the opportunity to find an answer to whether or not free throws had an effect on a team’s likelihood to win a game. To find this answer we broke the dataset into individual games and looked at the free throw stats from there. First, we looked at the amount of shots the winning and losing teams took on average per game.
As this image shows, the winning teams on average took more shots per game. The natural next question then was how many of those shots were made.
This graph shows the same trend, on average the winning teams tended to make more of the shots than the losing team. To fully understand these stats and their effect on wins, the final step for this was to compare the actual percentage of shots made per winning and losing team.
The trend is slightly less apparent here, but there is still a shift right on this graph for the winning teams. What all of these graphs show then is that having a good accuracy for shots made can help a team win a game.
With the data looked at from a per game view, the next step was to look at it from a per team view. When looked at per team there is a very broad amount of average shot accuracy numbers for each team.
The question this graph then begs is whether or not the teams on the right side of this graph tend to win more. However, when we look at average wins throughout the data, we found that throughout the percentage range, the percentage of games won tended towards 50% for both. Running an R^2 test then on the average shot percentage versus the average games won yielded a value of 0.2. Which suggests that when one looks at a team’s performance in the long run, having a high free throw accuracy is not enough to ensure that a team wins lots of games.
And so we have found that while being good at free throws is enough to help in an individual game, there are other much bigger factors that determine whether or not a team will perform well consistently.
Overall, our team found some very interesting insights. First, we looked into players and made two groups of players who gained the most points and who has a higher shooting percentage. Going into the project we were surprised to learn that players who are scoring more points don’t have a high shooting average. After, we looked into the shot count for the two groups. Surprisingly, both groups are more likely to make their second shot. This applied to every player in our groups. Also, Top Shooters are more likely to make their third shot. Next, we found that throughout the seasons free throws have consistently gone down. While comparing our grouped players their shot percentage has remained more consistent through the seasons. When looking into playoff seasons versus regular seasons we found that both seasons have a shooting average of 75%. To wrap up our project we looked into whether free throws can predict a team winning or losing and we found that winning teams consistently had more free throws in the game. Resulting in teams who had the freest throws being more likely to win.