Threes and Layups NFL Team Ratings, Explained

With the 2018 NFL season underway, I decided to release some work on my first NFL project: a team rating and prediction system.

Go to Ratings

Go to Weekly Picks

Motivating this project was a simple question.  How much should we adjust our thinking about a team based on the result of 1 game?  This has always been a question which has intrigued me.

We go into a new football season with some general expectations of the ability level of each team.  Many of our expectations come from their past performance, but some new information is also added with offseason signings, trades, injuries, etc…  Of course, there is a lot of uncertainty surrounding each team.

Then we witness week 1!  Some teams look like underrated breakout candidates, and others flop.  Hot takes abound.  But how much information can 1 game really give us?

How The Rating System Works

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Which Types of NBA Teams are the Best Win Total Over/Under Bets?

Though the NBA regular season is still 43 days away, it’s never too early to take a look at regular season win total over/unders! 

Rather than building a projection system and delving into whether specific teams are more likely to go over or under, I instead decided to determine whether or not five key predictors could in any way help us make predictions in general.  I will introduce the specific predictors in the next section.

To conduct my analysis, I looked at the over-under lines for all teams over the past six regular seasons.  I was able to find a combination of lines from Bovada and the Westgate SuperBook, using the articles cited in the reference section.  The dates for these lines ranged from October 5th to October 18, so they generally were recorded a week or two before the start of the regular season.  All other information used in this article, mainly the statistics used to calculate my predictors, came from basketball-reference.com.

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Democrats have a 23% Chance of Having a Majority on the Supreme Court in 2028

The retirement of Justice Anthony Kennedy and nomination of conservative Brett Kavanaugh to replace him is poised to give Republicans a more reliable majority on the Supreme Court.  But a 5-4 majority is a narrow one.  A Democratic victory in the 2020 presidential election coupled with the death of a single Republican appointed justice would produce a reversal in power on the Court.  The current state begs the question: precisely how likely are Democrats to regain the majority?

Despite the complexities of the legal and political processes that produce Supreme Court candidates, this question is surprisingly approachable statistically.  The central variables involved are not from the domain of legal theory or constitutional interpretation, but rather from areas familiar to a statistician: life expectancy, behavioral preferences, and election projection.  By consulting actuarial tables, the record on judicial retirements, and the history of presidential election results, I have endeavored to construct a quantitative understanding of the likelihood of changes of control on the Supreme Court.

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Which College Statistics Are Most Correlated With NBA Success?

The NBA draft is almost upon us! For the sake of curiosity, I decided to dig into the data to determine which college statistics have been most correlated with NBA success.

I will first give a few words on my methodology. I considered all players drafted in the year 2000 to the present whose age 25 season could have happened in 2017-18 or earlier. The age 25 season criteria is included because I measured player value in terms of Win Shares (a stat provided by basketball-reference) produced in the age 25 season. I chose to measure performance at this age rather than something like prime years (age 26-29) to allow more recent draft picks into the sample.

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Introducing 3-PIGS, a New Way of Understanding Three-Point Variability in NBA Games

The rise of the three-point jump shot in the NBA has been well documented. Over the past five regular seasons the average team three-point attempt rate has spiked from 24.3% to 33.7%, per basketball-reference. The analytically driven Houston Rockets actually attempted more three-pointers than two-pointers this past season!

In this three-point happy league, analyzing an individual game after removing the effects of “hot” or “cold” three-point shooting can be very informative. It allows us to see how well the teams played in all aspects of the game except three-point accuracy. We can strip aside a particularly unusual shooting performance and observe the “fundamentals” of a game, in a certain sense.  This is what Three-Pointer Independent Game Score (abbreviated as 3-PIGS) does.

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Are Past Games in a Playoff Series Predictive of the Next Game?

I recently was building a simple model to forecast an NBA playoffs series. As I was building the model, I realized that I was not taking into account the possibility that each team’s strength could change over the course of the series. If a team dominates games 1 and 2, we might reasonably expect them to have a higher likelihood of winning game 3 than we did at the beginning of the series.

But perhaps we should not alter out initial belief too much. After all, the 2 games in the example above is not a very large sample. Sheer randomness and recency bias may be causing us to shift our thinking too much. My initial hunch was just that; the general public overreacts too much to a few performances.

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An Analysis of the 2-for-1 Strategy in the NBA

The appropriately named “2-for-1” is a strategy utilized at the end of a quarter in which a team tries to time their shot attempts so that, as the name suggests, they get two attempts while their opponents only have one. To execute the strategy, a team usually pushes the ball up the floor to take a shot with about 30 seconds left in the quarter, thus ensuring that their opponents cannot hold for the last shot.

Intuitively, this strategy seems perfectly reasonable. Just like holding for the last shot, a team which executes the 2-for-1 is gaining one extra possession. Who would not want an extra possession? Well, we could imagine a scenario where the two possessions are so rushed that their expected value is less than the value of the one “normal” possession the opponent is allowed. For example, suppose we value a conventional NBA possession at 1.09 expected points but the two rushed possessions usually generate bad shots and are only worth 0.5 expected points each. Then it might make more sense to execute a “1-for-1” strategy and simply grant the opponent the last shot.

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