March Madness – 1st Round Probabilities

The following tables display our predicted probabilities for all 2017 first round games, separated by region. The play-in teams are marked with an * and will be updated as needed. As an example of how to interpret the tables, in the first row of the East Region table, our models give Villanova a 97% chance of beating Mount Saint Mary’s (and, conversely, Mount Saint Mary’s a 3% chance of beating Villanova). We have more content on the way, including 1st round point spreads and totals, as well as a look at how our numbers see the entire tournament field.

For more on our methodology, click here.

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Twins Rotation Preview: Hector Santiago

Previous installments in this series:

This week, we continue our Twins rotation preview by taking a look at Hector Santiago. Acquired from the Angels midseason last year, he figures to be a fixture in the Twins rotation for the 2017 season. It’s a good week to brush up on your Santiago knowledge, because you’ll probably be able to see him pitch in a meaningful game within the next couple of days! He’s on the Puerto Rican roster for the World Baseball Classic, and they’re playing three games in a row: they face Venezuela today, Mexico tomorrow, and Italy on Sunday.

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Similarity Score Tool

Select any 2016 drafted NBA player from the dropdown menu to view their top 10 most similar college basketball players historically. Similarity Scores provide insight into how a player will translate to the NBA based on how similar college basketball players in production, physical measurements and experience have performed in the NBA. For a background on the calculation of the similarity scores, see here.

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Similarity Score Methodology

Introduction

“Buddy Hield is the next Stephen Curry”
“Brandon Ingram is a poor man’s Kevin Durant”
“Andrew Wiggins’ upside is Carmelo Anthony, but his floor is James Posey”

So often when we talk about NBA players, we do it through comparisons to other players, and with good reason—comparisons are a good way to quickly convey lots of information about a player. For example, if I tell you that a player had a Box Plus/Minus of 7.8 last season, you might get a vague idea of how good he is. If I then tell you that player had 19.5 points per game, you might have a slightly better idea, but it’s still far from the full picture. But if I claim that this player is the next Chris Paul, it immediately brings to mind an idea of not only how good he is, but also his strengths, weaknesses, and overall playing style. Maybe your mind also queues up a mental highlight reel of Chris Paul-like plays, for good measure. Comparisons quickly give a complete picture of a player which would otherwise require taking the time to slowly digest each number in his stat line.

Also, they’re lots of fun!

We’ve created our own set of similarity scores to make comparisons using math for the purpose of prospecting players coming out of college. Our goal is to produce a useful complement to our PNSP model. Where our PNSP model answers the question, “How valuable will this player be?”, our similarity scores aim to answer the question, “Who will this player be like?”

For a glimpse of some player comparisons, you can check out Similarity Scores for 2016 NBA Draftees, here.
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Twins Rotation Preview: Kyle Gibson

This week, we continue our series on the Twins starting staff by taking a look at Kyle Gibson. For the series introduction and the first installment on Ervin Santana, look here.

Gibson’s 2016 season was a bit of a step backward; from 2015 to 2016, his ERA jumped from 3.84 to 5.07, and his FIP increased from 3.96 to 4.70. Nevertheless, Gibson is a lock for the 2017 rotation, and will try to bounce back. Let’s take a look at the tools he’ll use to do it.

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Twins Rotation Preview: Ervin Santana

Series Introduction

Last week, I introduced the Pitcher Count Predictivity Score, a new stat to measure pitch mixing. PCPS is a very general, simplistic measure. It has to be! There are so many different types of pitchers, with such diverse arsenals of pitches, that a stat must be very broad to apply to all of them. This broadness is an asset, in that it allows us to compare very different pitchers on a similar scale, but it also means that PCPS doesn’t tell the whole story about a pitcher.

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A Simple Way to Measure Pitch Mixing

On Sunday, the NFL ended its season with the Super Bowl. As soon as tomorrow, pitchers and catchers will report to Florida or Arizona to begin spring training. And in Minneapolis as I type this, it’s a balmy 42 degrees. I have no doubt that we haven’t seen the last of this Minnesota winter, but nonetheless, I’ll take it as a sign that it’s time to turn our attention to baseball. So let’s talk about pitchers.

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2016 NFL Playoffs Recap

After an otherwise forgettable first few weeks, this year’s NFL Playoffs were capped off by the greatest comeback in Super Bowl history, led by the league’s undisputed G.O.A.T, better known as Tom Brady. Facing a 25 point deficit part way through the third quarter, the legacy of the Brady-Belichick era was put on the line, and the light at the end of the tunnel was looking brighter and brighter for Roger Goodell. Little did he know, that light was a reflection beaming off of Brady’s fifth Super Bowl ring, and he’d soon be on the field coughing up the Lombardi trophy to the team he’s conspired against for the past several seasons. Although he dodged the bullet last year when the Broncos knocked the Patriots out in the AFC Championship Game, the four-game suspension he imposed on Brady to begin the year proved to only fuel the fire in the Patriots’ season of vengeance on the commissioner. Although our Models didn’t contain a variable of this nature, 5 of them correctly predicted the Patriots to beat the Falcons and our spread model had them winning by 9. For the playoffs as a whole, we had two models finish 10-1 (only missing the DAL/GB game) and our average Model finished 9-2. See the tables below for our predictions for all of this year’s games.
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