Heeeeeeeeeeeeeeeeelloooooooooooo everybodaaaaaaaaayyyyyyyyyyy!!! Welcome to my first data analytics blog post.
If you got that Fox Sports West reference then ultimate respect to you…If you know the name of the sportscaster who started his show with that line…you are a true southern California sports fan. But anyway, you’re here for data so let’s get started on the good stuff.
I decided to do this post as my inaugural post for a couple of reasons:
A) I am interested in the findings.
B) I had a hypothesis on how the data would turn out and wanted my first study to look like I actually found something.
The Low Down:
Basically what we are looking at here is the progression of a team’s field goal percentage from month to month as the season goes on. We’re trying to see at what point in the season teams shoot the best.
My initial hypothesis was that as the NBA season went on, teams would shoot better, making the graph a simple linear regression trending up. This was based on the reasoning that players and teams are rusty, still getting used to coaching systems, and simply not in game shape at the beginning of the year. As the season progresses, however, they start to play at a higher level – leading to a steady increase in shooting percentages.
However after a little bit of thinking, I revised my hypothesis to say the FG% would look more like a bell curve, with percentages highest in the middle of the season and lower at the beginning and tail ends.
Here’s my reasoning:
Start of the Season: Players are still getting their game legs and adjusting to coaching/systems, causing them to shoot a lower percentage (Same as the initial hypothesis).
Middle of the season: Players are in game shape, used to their systems and coaches, and the games don’t matter as much at this point – all things conducive to a higher percentage of shots going in. I call this portion of the season the ‘cruising’ portion.
End of the season: At this point of the season, the games matter much more as teams fight to get into the playoffs. Defensive intensity is ramped up which makes it harder for the average team/player to shoot a high percentage.
- Obtain a dataset with season long data – specifically we are looking at team field goal percentages and game dates
- Group the games by month (I tried analyzing on a game by game basis, but there was way too much game to game fluctuation for any real analysis)
- Once grouped by month, find the average field goal percentage shot each month
This graph shows the field goal percentage progression from the 2017-18 NBA season. As we can see, the data supports my hypothesis! FG% is lower to start the season, higher in the middle, then drops off again toward the end.
- There is a much larger jump from October to November than the drop from February to April.
- This may go to show that players getting used to their systems and getting back into shape has a bigger impact on FG% than the ramped up defense at the end of the year.
- October is a shortened month because the season starts in October, so the small sample size may be a factor in the large jump.
- Last year, players shot the best in February and the worst in October.
For now, it looks as though the data supports my hypothesis that FG% is a bell curve as the NBA season goes on. Of course, I’ve only pulled data from one season and to get a better picture we’d have to perform this study again over multiple seasons.
Thanks for reading! If you have any data related questions you would like answered, let me know and I’ll see what I can do!