Understanding the Statistics Behind Sports and Life

"I like to feel that I understand little things about sports."
-Bill James

Why SaberSmart?

Recently, we have found it increasingly difficult to keep up with the influx of advanced statistics serious fans, analysts, and sports commentators inundate us with throughout the baseball season, and even amidst our day-to-day lives.

This is our journey to understand the theory and meaning of the numbers behind the box scores as well as those all around us. Hopefully we can even help teach a little statistics and sabermetrics along the way!

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Recent Posts

Predicting who will win baseball games is a hard task. Baseball is notorious for its high degree of luck, regression to the mean, and the inability for anyone to judge a player’s “true” talent based off of only a few games.

Now, on August 21, 2019, the Houston Astros played the Detroit Tigers, in Houston. Just for some perspective, the Astros at the time had a record of 81-46, while the Tigers had a record of 37-86. If the two teams were in the same division, the Tigers would be 40+ games back of the Astros.

With the so-called “hard” trade deadline approaching fast for MLB Teams, (now in a matter of hours!), we decided to once again fire up our servers and use our Bayesian model to predict each team’s end of season record, and consequently their shot of making the playoffs.

This season eliminated August waiver trades, a past opportunity to grab a player once a team’s stance has been made clearer. July 31 now represents a true, hard trade deadline for the first time in Major League Baseball history.

Now that most of the NBA teams have passed the halfway point of the season, we recalculated our end-of-season win total predictions and playoff probabilities. In truth, we don’t think that the second half of the season matters that much, in terms of playoff probability.

Since the top eight teams from each conference make the playoffs, the likelihood that even the top four teams in wins per conference at mid-season would fall out of the top eight at the end of the regular season is minuscule.

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The Major League Baseball season has only been in action for around a month, yet some fans have already declared their team’s season as “over”. While this may be true for the Marlins, how can we use statistics to see how a team’s start will affect their overall season?

Here’s a silly article by CBS Sports: More than half of MLB teams are on pace to win or lose 100 games in 2018. If you are going to make outlandish claims, at least use math to support it.

On Twitter, someone pointed out that my predictions weren’t very revolutionary, as they matched the same conclusions that can be seen on Fangraph’s Playoff Odds website.

Fangraphs had Seattle making their first postseason since 2001 by snagging the second wildcard, the Nats winning the NL East despite being 5.5 games back of first at the time of data collection, and the Brewers/Diamondbacks going 1-2 with the NL wildcards.

A common dream of most baseball aficionados is to visit every ballpark, stepping inside with their own two feet, feeling the history, and seeing every team live.

After seeing this article on an itinerary that would allow travelers to see every single national park in the 48 contiguous states on a road trip without wasting any time, I wondered how this methodology worked, and how it could be applied to a ballpark journey.

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Let us go back to a simpler time, when the Astros had yet to win a World Series, the Las Vegas Golden Knights were preparing for the expansion draft, and the hype for The Fate of the Furious was taking over the internet. I am, of course, talking about March 2017.

I decided to step back in this article and examine how this methodology works in greater detail, as well as how it applies to a season where we already know the results, the 2017 season.

The 2017-2018 NBA season culminated in a final showdown between the Cleveland Cavaliers and the Golden State Warriors for the fourth year in a row. While those two teams were popular Finals picks, we wanted to see if the other 14 teams that made a postseason berth could be predicted.

No statistic quite tells the whole story of a teams’ season like postseason odds, or the probability of making the playoffs...

For those of you that have followed my blog in the past year and a half, one statistical technique that you may have noticed I commonly use is Monte Carlo simulation.

While I usually skim over the basics of Monte Carlo simulation to get to the meat of my analysis, I want to take the time in this post to delve into this method a little more deeply, and show by example, the immense power of the Monte Carlo method.

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