In this post, we will learn about the Elo rating system. This system was originally developed to rate chess players, and has become a very popular tool to analyze many sports. We will look at how to apply the system to basketball to rate NBA teams. My goal is to show you the key assumptions and math behind Elo ratings, and how to implement the system in Python. We will use Elo ratings in upcoming posts to examine NBA playoff match ups.
My other goal is to point out some of the limitations of Elo ratings. In future posts, we will examine ways to address these limitations, and look at alternative ratings systems that try to do a better job.
Continue reading “Elo Ratings for NBA Teams”
We’ve been looking at NCAA first round upsets in the prior two posts (see here and here). Now that we know the result of the play-in games, it’s time to make some decisions.
Here are the upset picks I made after a lot of discussions with my sons.
|Lower Seed to beat||Higher Seed
|(13) Buffalo||(4) Arizona
|(12) South Dakota St.||(5) Ohio St.
|(11) Loyola Chicago||(6) Miami (Fla.)
|(11) Syracuse||(6) TCU
|(10) Butler||(7) Arkansas
|(9) North Carolina St.||(8) Seton Hall
|(9) Florida St.||(8) Missouri
As we’ve discussed in the prior posts, the 8-9 games should never really be considered upsets.
I don’t think many of these upset picks are very controversial, perhaps with the exception of Buffalo over Arizona. It was hard to pick 3 and 4 seeds to fall in the first round this year. For me, Buffalo over Arizona is ultimately a gut call even factoring in DeAndre Ayton. My sons don’t agree with me on this one.
Let’s the Big Dance begin. Good luck with your picks.
This post continues where our previous post on NCAA first round upsets left off. We will look at Ken Pomeroy’s KenPom college basketball ratings and see how they can help us predict wins and losses. By combining the KenPom data with the NCAA tournament game history from our previous post, we’ll see whether the KenPom data would have helped predict first round upsets in previous NCAA tournaments.
Continue reading “Ken Pomeroy Ratings and First Round Upset Picks”
March Madness is almost upon us! Are you ready to fill out your brackets?
It’s hard for your bracket to do well if your picks lose in the first round. Although some approaches to bracket selection start with the Final Four and work backwards, we’ll start with the first round. How many upsets should you pick in the first round to maximize your chance of success? Read on to find out!
Continue reading “March Madness First Round Upsets”
This is our fourth post about home court advantage in the NBA. If the home team has a higher win probability, that advantage should be evident in at least some of the box score statistics. Home court advantage could result from better offense, better defense, or a mix of the two.
In this post, we’ll examine how basic box score statistics vary between home and away games.
Continue reading “Which Box Score Stats Contribute to Home Court Advantage?”
This post looks at whether particular NBA teams have demonstrated significantly different amounts of home court advantage over time. This is challenging, since team quality varies significantly over time.
The punchline? There is only very weak statistical evidence that teams have persistent and measurable differences in home court advantage relative to the league average.
Continue reading “Do NBA Teams Have Different Home Court Advantages?”
In this post, we are going to take another look at scraping NBA team information from Wikipedia. We will also see how generate a map of NBA arena locations.
Continue reading “Scraping NBA team information from Wikipedia (Revisited)”
In our our previous post about NBA home court advantage, we saw that home court win percentages varied over the past 21 seasons, but have averaged around 60%.
In this short post, we’ll try to drill a little deeper into the distribution of home court win percentages.
Continue reading “A Deeper Look at NBA Home Court Win Percentages”
Data is central to any analytics project. In Python, by far the most commonly-used package to manage data is
pandas. In this short post, I will offer a few suggestions for those of you who want to get up to speed using
pandas or take your skills to the next level. There are a huge number of resources out there. Hopefully this will help you choose where to start.
Continue reading “Learning (or Improving at) pandas”
In a previous post, we scraped NBA matchup data for the 1996-97 through 2016-17 seasons. We also noted that, during that time period, on average the home team won roughly 60% of home games during the regular season.
This is a big deal. An average team should win 50% of the time on a neutral court, but history shows that home court is worth a roughly 10% boost in win probability during the regular season. We’ll focus on the playoffs in a later post. Home court clearly matters a lot in the NBA post-season as well.
In this post, we’ll start by reviewing some of the main explanations for home court advantage in the NBA. Then, we’ll take a quick look at how the advantage has varied since the 1996-97 season. We’ll also look at how the advantage shows up in point differentials.
Continue reading “A First Look at NBA Home Court Advantage”