Phones pinged like 3-point attempts in the playoffs. LeBron James informed the Cavs that he would decline his option and, indeed, become a free agent. Where could the King fit in his quest to sit atop the NBA championship? While LeBron can take a team far, we saw this season that the road, while possibly long in the post-season, is one that, to LeBron’s dismay, might ultimately have a Golden ending.

Where does LeBron fit? Let’s look at assists. In the chart below, you’ll see an assist map for the 2017-18 Cleveland Cavaliers. We’ve highlighted only the region containing assists to (yellow) and from (wine) the King. You’ll see he assisted many more times than he received one, indicating his skill as a solo player who often needed only an inbound pass. LeBron contributed 43.48% of the total assists in this group of players, where you may have noticed we removed players with smaller contributions to simplify the graph. No other player’s percentage of assists even is in double digits. Contrast this to baskets made off assists, Kevin Love is the highest with 16.3% following by LeBron with 15.2%.

If nothing else, the chart clearly shows the dominance LeBron had in the Cavalier’s offense. Note that LeBron was able to average 27.5 points per game last season with such a low baskets made off assists rate. Still, should we sweep this aside given the 0-4 record of the team in this year’s NBA Finals? With this in mind, let’s consider the 2015-16 season with Kyrie Irving adding his prowess to the offense that won the finals in historic fashion.

Indeed, there is more balance with LeBron still playing a huge role. Now, LeBron contributes just over 29% of the assists followed by Kevin Love, Kyrie Irving and Matthew Dellavedova all contributing a percent of assists in double digits. Similarly, LeBron shot 16.6% of the baskets off assists, slightly behind Kevin Love who shot 18.7% and leading J. R. Smith who shot 15.2%. LeBron benefited from a more distributed load. Indeed, the help LeBron may seek this off-season should not only lighten his scoring load, but also open up his own scoring. He could feed and be fed.

Given this, what table do the Lakers set? How does their offense line up, at least with last year’s players?

Interestingly, they were fairly even, albeit with Ball spending time injured. Nonetheless, there is balance to the team seen clearly in the graphic and backed up by the numbers. Could this signal the reign of the King?

Keep in mind that the 2015-16 Cavaliers had distribution among All-Star players. Here enters the recent rumors whispering a Kawhi Leonard trade. Let’s turn to Texas and consider the Spurs from a much less Leonard-injured season, specifically the 2016-17 season. Could Leonard feed the King or receive the blessings of his assists?

We see Leonard’s high contributions overall and in both assists and baskets off a pass. Further, he shared the load with Parker and Aldridge. Could this signal the return to LeBron’s championship basketball?

What do you see in the analytical tea leaves? Keep in mind the trades that might be necessary to bring LeBron and Kawhi to LA. Could we see a return to Showtime basketball? Before long, we’ll learn where passes zip to and from LeBron. Time will tell and even then teams evolve, but then again, isn’t that why we watch both in and out of season?

Image adapted from: https://www.flickr.com/photos/keithallison/31914841664

This piece is co-authored with Matt Wang.

The post Feeding LeBron first appeared on 'CATS STATS.

The 2017-18 regular season ended with a Wildcat splash! In the final week, Davidson beat Rhode Island as the Cats truly went wild on their home court. Earlier in the week, the Cats went to triple overtime with Peyton Aldridge scoring a career high 45 points, which surpassed Stephen Curry’s career high of 44. It was more than a big week, it was a huge year for Aldridge. Let’s look at the numbers.

First, Peyton ended the season leading the A-10 in points per game with 21.3 PPG. He had a number of huge weeks throughout the season landing him five A-10 Player of the Week honors. And, he notched 5 double doubles.

By, let’s dig a bit deeper and compare Peyton to other players with big seasons. Let’s look at Points Per Game, turnover rate, percent of minutes player, percent possessions, and percent shots. We’ll compare Peyton to Jaylen Adams, Jared Terrell, and Justin Tillman.

Note, Peyton’s stats lead each category. Not only does he top the stats but in many cases, he’s leading by a lot! The numbers make quite a case for Peyton as Player of the Year in the A-10!

It’s always great to be a Wildcat. This season it was truly great to watch #23 with such a notable season!

The post Peyton By the Numbers first appeared on 'CATS STATS.

We inch ever forward to the Super Bowl and the smashing of pads between the Patriots and Eagles. You flip through the channels and the hum of football analysis grows until that deafening cheer as the ball is kicked off to begin the game where one team stands with the Vince Lombardi trophy lifted high. Who will win? What should we look at? What stats really matter?

Let’s turn to an ESPN article appropriately entitled The NFL stats that matter most by Bill Barnwell. Let’s see how these statistics stand up over several years of NFL data and how our championship teams fared against these metrics.

Let’s look at the stats.

DVOA (Defense-adjusted Value Over Average)

This stat is given as percentage and measures a team’s success on a given play versus the expected performance over time. A team with a 10 percent DVOA is that much better than the league average on a play-by-play basis.

How do past Super Bowl teams perform? At the bottom of this article, you can explore our interactive tool made with Tableau. For now, here are our insights. First, three of the past five Super Bowls featured the team with the highest DVOA in the regular season. The 2013 Seattle Seahawks and 2016 New England Patriots finished the regular season with the highest DVOA, and went on to win the Super Bowl. However, this was not the case for the Seahawks in 2014.

How about this year? You’ll see the matchup between the Philadelphia Eagles and the New England Patriots as the narrowest margin in DVOA between Super Bowl opponents in the last five seasons.

Point differential

The Pythagorean expectation is a measure of how many games a team should have one and even how lucky a team appears to be if they’ve won more games than they should have, under this metric. Some say it isn’t luck — it is coaching! Could it be that the Patriots or Eagles are lucky enough to outperform their stats? Are one of the teams about to have their luck run out?

First, let’s peer back. Notice AFC Super Bowl teams in the past five seasons have finished the regular season “luckier”, meaning they won more games than Pythagorean expectation would have predicted. The only time this wasn’t the case was in Super Bowl 50, a matchup of the two luckiest teams in football that season.

So, what about those Eagles and Patriots? Both rank in the top 10 luckiest teams this regular season, but both teams won only one game above Pythagorean expectation. One team’s luck will run out very soon.

More gridiron stats

Let’s turn to quarterback statistics. There are several that you’ll find. First and most straightforward is yards per attempt. There is also Total QBR which, while having flaws, gives a one-number metric to compare quarterbacks. So, how about Brady and Foles? How much data do we have on Foles and what does it say?

In the Tableau application, you’ll see a comparison of Brady and Wentz. Isn’t that the conundrum of the playoffs? How will the Eagles do with Foles continuing to take snaps? Time will tell. You have much to explore with statistics like Catch Rate, Running Back Success Rate and QB Hits. Are these the stats that really count? Here is your dashboard to explore the stats and come up with your own analysis! Which stats do you think matter? Which might give you more insight on what we’ll be talking about Monday morning?

In the end, explore the stats, look for patterns, be your own sports analyst and find an insight that you can share at a Super Bowl party that might momentarily silence your friends amongst the inevitable clamor of the big game.

This is article is co-authored by Scott Teal, Sr. Data Artist at Tableau, and Dr. Tim Chartier. This is part of the Cats Stats series on using ESPN in the Curriculum.

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