STATS NBA 2017-18: An Advanced Look Into The Offseason Shakeup

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The ground has most certainly shaken in the East with the most unlikely of trades between conference frontrunners.

Kyrie Irving and Gordon Hayward have landed in Boston to join forces with the latter’s former coach. LeBron James has been dealt an unlikely top-three scorer.

Transcontinentally, 2016-17’s DIYers James Harden and Russell Westbrook have been given some serious help. Or is it the other way around: Chris Paul, Paul George and Carmelo Anthony still don’t have a true last name among them, but are they the main beneficiaries?

Yeah, there’s a Jimmy Butler-Tom Thibodeau reunion on in Minnesota, but the Timberwolves also brought in a point guard who may better suit their bigs.

Lob City is no more, but what it ultimately comes down to is everyone’s still chasing Dub City. Come along with STATS and the most reliable and fastest NBA data available as we take a deep diagnostic dive to weave select stories of how the pursuit may go down in 2017-18.

Player Tracking and Deep Learning Innovations Lead to Greater Interactions with Data

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Fans regularly consume STATS’ data without knowing the original source of the information, and that isn’t necessarily changing as the company explores future applications of richer data. What is different in 2017 from 1987, 1997 or even 2007 is STATS’ specialization from often drawing the line at providing sports information to more properly contextualizing it for clients.

One of the leaders making that happen at STATS emerged from behind the scenes to speak with Sirius XM Wharton Moneyball hosts Adi Wyner and Shane Jensen earlier this month. Patrick Lucey, STATS Director of Data Science, reflected on five years of STATS SportVU tracking data collection and provided insight on the answers now coming from growing sample sizes and industry advances.

STATS began tracking NBA games with its optical system SportVU half a decade ago, which happened to coincide with what Lucey marked as a particularly interesting time in sports science.

“Something happened in 2012. Deep learning came along,” said Lucey, co-author of the 2016 MIT Sloan Sports Analytics Conference first-place research paper on patterns of player movement and ball striking in tennis.

“It’s like a perfect storm. I like to think of it as a three-legged stool. First of all, for deep learning to work, you need three things. The first thing is you need a lot of data. Luckily at STATS, we have that. The second thing is you need the computational power. … The third thing is having the people to actually know how to work or set up the network.”

For nearly two years, Lucey has sat atop that stool at STATS and helped expand player tracking into additional sports with an emphasis on the global game of football. Plenty of his current work, including his 2017 MIT Sloan Conference runner-up paper on shooting styles in basketball, has involved finding the proper ways to ask and answer the what-if questions that weren’t confrontable in STATS’ earlier years.

“The big thing – and this is what we can do really well – we can contextualize the data,” Lucey said. “…You can ask us specific questions. You can say, ‘Well, what’s the likelihood of this player making that shot? Or, what happens if I switch that player with another player?’

“So, we can really do fine-grain simulations and ask those what-if questions. … We have the box score or we have these players’ stats, but (I think the next stage in analytics) is to simulate these kinds of specific plays and see what a different player will do in these situations. In a given situation, we can model that context and we can give more precise answers because we understand the data in those situations.”

The particulars of body pose have become one of his key interests. Specifically, advances in player tracking – and the potential for more accurate predictive analysis – lies in an increased emphasis on measurements that can go beyond using a basic center of mass to establish the XY coordinates in optical tracking.

“Once upon a time, we could capture body pose, but we had to do that in a lab setting,” Lucey said. “… Now we can actually do it in the wild.”

Lucey’s team is constantly honing in on the next big thing in its field, and his view for how performance will be evaluated more closely in the future might surprise some. It’s not necessarily about creating fresh analytical metrics. It’s about the relationship between people and technology.

“I don’t actually think new metrics is the way to go,” Lucey said. “I don’t think that’s the future. I think it’s a symbiosis between a human and a computer. So can we develop new technology to help a domain expert do their job better. I think that’s really the next step in sports analytics. Just enabling, creating this kind of technology just to help coaches or analysts or people at home to be able to ask these what-if questions. I don’t think we’re that far off.”

Tough Shots No Problem for Cavs’ Smith in NBA Finals

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Irving, Durant Also Stand Out in STATS SportVU’s Tough Shot Score Metric

There are many ways to measure the greatness of players like LeBron James, Kevin Durant and Kyrie Irving, but perhaps the simplest involves no advanced stat at all.

They make it look easy. Durant gliding down the left wing and stopping on a dime for a pull-up 3. James darting into the lane and spinning past his defender for a lay-up in one silky smooth motion. Irving leaving a would-be road block in his dust after a series of crossovers and using the perfect amount of English to will the ball in off the backboard.

It’s not.

STATS has created a metric called Tough Shot Score with the use of its revolutionary STATS SportVU cameras that measure how difficult a given shot is based on a variety of factors: the position of the defender, where the shooter is on the floor and whether he’s moving or stationary. The score is normalized from 0-100, with 100 being the most difficult shot possible. Here’s a little of what we found out about just how tough some of the NBA Finals’ biggest stars had it on basketball’s grandest stage.

 

Watch J.R. Smith play for any five-minute stretch of his career and it’s obvious that he doesn’t mind taking tough shots. It’s almost like he prefers them, often dribbling into what seems to be a more difficult, suddenly contested shot than what he had when he caught the pass.

Of the eight top scorers in the Finals, Smith’s regular-season Tough Shot Score (0.61) was easily the highest. So it shouldn’t be surprising that among the 11 players in the Finals with 20 field-goal attempts, Smith’s TSS (0.62) was the only one above 0.58.

Player Total FGAs Tough Shot Score
Smith (CLE) 36 0.62
K. Thompson (GS) 70 0.58
Curry (GS) 87 0.57
Irving (CLE) 123 0.54
Love (CLE) 67 0.54
Durant (GS) 106 0.54
James (CLE) 117 0.50
Green (GS) 55 0.50
Iguodala (GS) 34 0.49
Livingston (GS) 28 0.47
T. Thompson (CLE) 22 0.44

 

Not that the level of difficulty seemed to bother him. After going scoreless from 10:26 of the first quarter of Game 1 until Game 3, Smith was unconscious in the series’ final three games, going 17 of 27 from 3-point range. That includes going 7 for 13 when his defender was contesting within 0-2 feet (very tight) or 2-4 feet (tight).

There’s one player in the series who took more tightly contested 3s than Smith, and he happened to be the one who wound up raising the Finals MVP trophy.

Durant took 312 3s during the regular season, less than a third of which were tightly contested. He hit just 31.4 percent of those, a big drop from the 40.3 percent he hit when open (4-6 feet) or wide open (6-plus feet).

In the Finals? Durant was masterful in transition and was a huge factor defensively, but there was one major reason he wound up sharing a handshake with MVP namesake Bill Russell at the end of Game 5. Of the 38 3s Durant took against the Cavs, 24 of those (63.2 percent) were tightly contested. He made 13 of them, including the Game 3 dagger over James that gave Golden State a lead it would never relinquish. It stands out as the series’ signature moment. All that added up to was a 0.65 TSS from 3-point range, which was actually a shade under his regular-season TSS from beyond the arc (0.66).

Irving had more than a few eye-popping performances in the series, going 7 of 12 from 3 in the Cavs’ lone win. But it was what he did around the basket – particularly in Cleveland’s Game 3 loss – that really stood out. Irving went 14 of 28 on very tightly contested 2s in the series, giving him 11 more attempts with a defender draped all over him than James and two more than the two most heavily guarded Warriors (Durant, Draymond Green) combined.

Only Eric Bledsoe and the Splash Brothers themselves shot better than 50 percent on 2s when tightly contested during the regular season, so for Irving to hit that mark in the Finals while wearing Klay Thompson like a compression shirt is quite a feat. Yet his TSS from inside the arc was higher during the season (0.51) than in the Finals (0.49), so Irving’s Finals’ circus act wasn’t really much different than what he’s been doing since October.

Brett Huston is a Senior Editor at STATS LLC. Contact him at bhuston@stats.com or on Twitter at @BHuston_STATS.

2017 NBA Finals Preview

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The top of any list of great sequels in cinema could easily double as a list of some of the finest – and most financially successful – movies ever made. The Empire Strikes Back, The Godfather: Part II, The Dark Knight, Terminator II, Aliens – we could go on.

That’s typically where the creative juices stop flowing.

Sure, there are some noteworthy third acts. The Return of the King is the best Lord of the Rings movie, though that’s more properly viewed as one colossal installment instead of three smaller ones. Indiana Jones and the Last Crusade both made up for the weirdly terrifying Temple of Doom and was popular enough for Harrison Ford to keep playing the character as a senior citizen. Goldfinger was arguably the best James Bond movie.

There haven’t been many third acts in the sports world, which is just one small reason why Cavs-Warriors: Part III should be so compelling. The Lakers and Celtics have met in 12 Finals, but never three in a row. It’s a first for the NBA and just the fourth such threequel in American professional sports history, the likes of which haven’t been seen since the Red Wings and Canadiens battled for three straight Stanley Cups in the 50s.

We’ve already detailed the lack of big-screen triumphs when it comes to third acts, but the success of screens could have everything to do with a Finals that’s rightly drawing as much hype as anything since the days of Jordan, Magic and Bird. Let’s take a look at what the Cavs need to do to repeat and what the Warriors can do to make these Finals more Rise of the Machines after last year’s epic Judgment Day.


When you have 30 percent of the All-Stars from a few months ago in one Finals series, it’s easy to get excited about matchups. Will Steph Curry or Klay Thompson guard Kyrie Irving? Does LeBron have no choice but to spend most of his time checking Kevin Durant? We know Draymond can cause issues for Kevin Love, but can Love be matched up with him on the other end?

Those seven will all find one another at some point, but the winner of this rubber match figures to be the team that consistently creates, and then takes advantage of, the most opportune mismatches.

That the Warriors move the ball and move away from the ball better than any team in the league is no great secret. It’s what almost every team in the league dreams of emulating and one day building themselves. Setting screens is still a major part of an offense that hums like a Ferrari when it’s at its peak, but ball screens are a different story. Golden State set 3,324 of those in 2016-17, per STATS SportVU, its third straight season bringing up the NBA rear in the category. Orlando was 29th, yet the Magic set more than 4,000.

There’s less movement in Cleveland’s offense because it’s less necessary. Possessions can come to a screeching halt in the final 10 seconds of the shot clock and Irving and James can save the day as few individual players can. Irving was the best isolation player in the league this season at 1.12 points per ISO, and he’s been even better in the playoffs. James can’t blow by defenders 1-on-1 like he used to, but his 42 percent success rate from 3 in the postseason adds a more complicated wrinkle for opponents than Batman suddenly wielding an assault rifle would for Gotham miscreants.

The Cavs relied on ball screens to generate offense less this season than they had in the past two, funneling through around 57 per game instead of the 65 or so they’d used in James’ first two seasons back home. Whatever way you slice it – and given the overall levels of talent and execution, this shouldn’t be a surprise – both Cleveland and Golden State get a lot out of their screens. Individually, the Warriors were third in the regular season in points per play (a screen that results in a field-goal attempt, foul or turnover by either the ballhandler or screener) at 0.934; the Cavs were fifth (0.925). As far as team points per possession – this adds in the other three offensive players on the floor as potential factors post-screen – Golden State was fourth (1.12), a tick ahead of Cleveland (1.11).

But those numbers take into account Derrick Williams setting a pick for Kay Felder on a cold February night in Minnesota or James Michael McAdoo trying to free up Patrick McCaw on a November back-to-back in Milwaukee. Let’s eliminate some of the noise and concentrate on what both teams should be focusing on – and what they must work to avoid at all costs.

Golden State DO: Get Curry/Draymond rolling

There was no Love in the 2015 Finals and there was no Irving for the final five games, and while those two are a generally dubious defending combination on ball screens, the Curry/Green combo likely couldn’t have done much better if they were both on the floor. Those six games featured 85 Green screens for Curry, which resulted in the Warriors eviscerating the Cavs defense for an average of 1.26 points.

Fast forward to 2016 and it turns ugly for Golden State. Seven games, a total of 39 Curry/Green ball screens and just 0.78 team points per action.

Green is fronting for Curry 6.7 times per game in these playoffs with excellent results: 1.30 team PPP. If that number stays in that vicinity – like it did two years ago – start sizing up the Warriors for their rings, and perhaps 16-0.

Cleveland DO: Target Curry when he’s guarding the screener

Irving has been known to struggle when he’s checking the ballhandler in the pick and roll, often never finding his original man or the roller and easily providing the opposition with a 2-on-1 toward the hoop. But that Irving/Love combo we discussed a few paragraphs ago? They actually defended quite well when put on an island in the 2016 Finals. There were 19 ballhandler/screener combos that defended at least 10 screens last June, and Irving/Love was by far the MOST effective despite getting torched overall in the postseason (1.31 PPP). Irving fared pretty well in the Finals when paired with Tristan Thompson as well.

Ballhandler Screener Screens Defended Team PPP
Irving Love 39 0.59
K. Thompson Green 12 0.73
Iguodala Livingston 14 0.77
Livingston Green 11 0.78
Irving T. Thompson 41 0.85
Curry Bogut 18 1.00

As for Curry, Cleveland preferred to have whomever he was guarding set the pick for the ballhandler. With Curry already banged-up to some degree in the Finals, the Cavs were physical while guarding him and made him work overtime at the other end. Curry was involved in 88 screens as the screener, nearly 50 more than the Warriors made Irving take on. A look at the difference in how both point guards were attacked in the pick and roll in last year’s sequel:

Player Screens starting on ballhandler Team PPP Screens starting on screener Team PPP
Irving 118 0.805 33 1.06
Curry 68 1.096 82 1.07

On Christmas Day in Cleveland, the Cavs ran Curry through nine more with him initially on the screener, scoring 12 points. Klay Thompson was the targeted on-ball defender – often on Irving – with Cleveland putting him through 28 screens and scoring 40 points. Overall, the Cavs celebrated their comeback win at The Q with 75 total points (1.19 team PPP) as the result of screens – 51 more than Golden State (0.71).

Cleveland DON’T: Let Iman Shumpert get screened into submission

The Warriors’ holiday in Northeast Ohio may have been dampened, but they took out seven months’ worth of frustration on the Cavs three weeks later in Oakland. Golden State used 46 ball screens in this one and particularly attacked Iman Shumpert on the ballhandler, often when he was checking Curry. Ten screens of Shumpert led to 22 Warriors points, further lending credence to this stat: In the 128 minutes Shumpert was on the floor in the 2016 Finals, the Cavs were outscored by 13.4 points per 100 possessions. In the 208 he sat, Cleveland enjoyed a plus-9.1 edge.

Golden State could drive Shumpert off the floor entirely in these Finals. In theory, he’s an ideal guy to stick on Curry or Thompson to hide Irving for a bit, but in reality he tends to get lost when he’s asked to do more than guard someone 1-on-1. Richard Jefferson played a key role against the Warriors last season and seems more suited to have a chance of defending Durant than Shumpert. With Kyle Korver a potentially vital offensive piece to stretch the floor, Shumpert may wind up a DNP-CD (can’t defend).

Golden State DON’T: Ignore Kevin Durant as a ballhandler

Let’s get to the elephant in the room of why many expect this series to be short. The Warriors added one of the three best players in basketball at the expense of Harrison Barnes, who went 5 for 32 from the field once Golden State went up 3-1 last year.

As we’ve covered, the Warriors aren’t going to rely nearly as much on the ball screen as the Cavs. But when things start to break down – particularly in the fourth quarter – there will be instances when it could be a necessity.

Logic tends to dictate that should a critical Golden State possession become bogged down, Durant will ISO, Curry will launch a 3 or, perhaps, Durant will come to the ball and screen for Curry. But there’s another option.

Durant has an awfully good handle himself. Curry screening for him should allow KD a moment to turn the corner and pop away from the secondary defender for an open 3. And if Curry can’t get free, Durant proved during the regular season that he was fantastic finishing in these situations. Of the 144 players who participated in 300 screens as the ballhandler, only Wilson Chandler and Paul George scored more points per individual screen than Durant (0.48).

It’s been even more absurd during the playoffs. Durant’s 0.66 average is a full tenth of a point better than any of the other 46 players who have participated in at least 50 screens. From a team perspective, the Warriors’ 1.36 PPP off screens with Durant as the ballhandler is second – and the chart below shows how infrequently that’s used compared to some of the other big names at the top.

Ballhandler Screens Team PPP
Stephenson (IND) 56 1.38
Durant (GS) 77 1.36
James (CLE) 271 1.35
Leonard (SA) 222 1.28
Curry (GS) 256 1.25

 

They’ve only broken the Curry-screening-for-Durant combo out 13 times during the playoffs but it’s led to 21 Warriors points, and frankly, there was no need to even do it that much. It’s a wrinkle that Steve Kerr and Mike Brown have largely been saving to unleash only when they need it, and that alone should terrify the Cavs.

Cleveland and Golden State DO: Get the big men involved

There have been 72 two-man combos that have run at least 30 screens in the playoffs, and the top two involve, as you might expect, James and Curry. But the other half of those equations probably isn’t who you’d expect. JaVale McGee has teamed up with Curry for 52 screens that have resulted in 1.47 Warriors PPP, tied with James and Tristan Thompson for the most effective in the league this postseason.

The James and Thompson combo has been a special kind of deadly on their 112 screens. James has hit 7 of 14 3s directly after Thompson frees him up, and the duo is 31 of 53 (58.5 percent) overall immediately after Thompson screens for James. Thompson is one of the league’s best at rolling off a screen and flushing an alley-oop from James or Irving, and he and James went for an impressive 1.14 PPP in last season’s Finals as well.


There you have it. There’s no shortage of storylines in the most star-studded Finals since Lakers-Celtics was in its mid-80s heyday. Durant’s chasing his first title. LeBron is chasing MJ’s legacy. Curry and Green are seeking Finals redemption. Klay Thompson wants to prove his subpar playoffs so far have been a fluke. Love wants to show that he can play – and play effectively – against the Warriors.

Golden State knows what’s coming. It’s up to the Warriors to keep Cleveland from catching them in bad ball screen combos while picking and choosing their own spots to use them in an offense that rarely does.

In a third act worthy of the big screen, we’re about to find out how big the screen can be.

Applying 3D Modeling to Player Performance

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STATS, the world leader in sports intelligence, has created a new tracking and analysis method that has powerful applications in terms of determining the future success of sports teams. Using 3D mapping to plot players’ every movement, each shot, step, dunk, or pass can be captured in detail not seen before. This data tells the full story of the game – which position had the highest likelihood of scoring points, why a certain style was successful or how a blocked pass could be avoided in the future.

The current STATS SportVU Basketball Data system reveals data on the types of plays teams run, and estimates on their future success. Six cameras are installed throughout an arena, capturing the position of players and the ball a remarkable 25 times per second. However, the new 3D model can provide an even deeper look. It can provide key insights into each player’s signature movements across a variety of plays by accounting for differing physical attributes as well as tracking what moves are unique to each player.

This 3D modeling approach can also equip STATS to flag potential athlete injuries. By using models created specifically from previous player behavior, researchers can identify when a player’s technique falls outside of the expected model and could possibly lead to harm. After an injury has occurred, the same method can be used by tracking if a recovering player is mimicking his previous actions.

The data that can be gathered using this method compared to earlier methods could be thought of as the difference between a standard pedometer and a Fitbit. Our previous ability to track and analyze information provided a solid, streamlined picture on what mattered. Now, we can expand into an entire new level of understanding and assessment on the performance of players and what this means for their teams’ success.

Analyzing a Potential NBA Finals Rematch

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Before the season began, the Warriors (86 percent) and the Cavs (48 percent) had the best chances of earning the top seeds in their respective conferences, per FiveThirtyEight. As of February 17, right before All-Star weekend, both teams’ probabilities had increased to 95 percent and 77 percent. The highly desired NBA Finals rematch was looking promising.

The Cavs, of course, ended up finishing second after dropping their final four regular-season games, twice resting LeBron James and seemingly ceding that top spot to the Celtics. Unexpected, sure, but hardly earth-shattering considering they went .500 in their final 46 games.

Perhaps the most shocking FiveThirtyEight statistic suggests that, going into the playoffs, Cleveland’s chances of making it to the Finals stood at a mere 11 percent (to Golden State’s 66). Betting markets were far more bullish on the Cavs, giving them approximately a 75 percent chance to make it out of the East despite their underwhelming regular season.

The Cavs have doubled their FiveThirtyEight chances after sweeping the Pacers in Round 1, but they’re still the East’s third-likeliest team to make the Finals (through Monday’s games), behind a pair of teams that are far from a sure thing to make it out of their opening series:

  • Celtics (26%)
  • Raptors (23%)
  • Cavaliers (22%)
  • Wizards (16%)

Bet you didn’t see that one coming.

Next up is either Toronto or Milwaukee, though the Raptors swung things considerably in their favor with Monday’s 118-93 rout. Should the Cavs be rooting for a playoff rematch against Toronto or the young Bucks to break through?

Young and inexperienced, Milwaukee has given Toronto fits at times with its athleticism on defense, posting a 100.1 defensive rating that’s the playoffs’ second best behind only the Warriors’. The return of Khris Middleton – who didn’t face the Cavs in the regular season – mixed with the ever-growing confidence of Giannis Antetokounmpo could hand Cleveland’s already-shaky defense some problems if the Bucks advance.

Antetokounmpo played all four regular-season games against the Cavs and averaged 24.0 points while getting to the free-throw line 43 times. He had 34 points, 12 rebounds, five assists and five steals – Anthony Davis was the only other player to post such a line – in Milwaukee’s only win in the series, which also happened to be the lone time LeBron James, Kyrie Irving and Kevin Love all played.

The defender who gave the Greek Freak the most issues might surprise you.

Offense Defense Games Matchup Time Points FGM-FGA FG% FTA Drives Drive Points
G. Antetokounmpo Richard Jefferson 4 15:29 6 2-12 16.7 4 4 1
G. Antetokounmpo LeBron James 4 9:20 11 3-6 50.0 4 3 2
G. Antetokounmpo Iman Shumpert 4 7:12 9 2-3 66.7 5 5 5
G. Antetokounmpo Derrick Williams 1 3:52 4 2-4 50.0 0 2 2
G. Antetokounmpo J.R. Smith 2 3:32 9 2-3 66.7 6 2 3
G. Antetokounmpo Tristan Thompson 4 3:31 9 4-8 50.0 1 3 4
G. Antetokounmpo Kyrie Irving 4 3:07 6 2-3 66.7 6 5 5

Richard Jefferson fell out of the Cavs’ rotation by the end of their series against the Pacers, but it’ll be interesting to see if Tyronn Lue brings him back in a potential Bucks matchup.

Jefferson also did a nice job against DeMar DeRozan as the Cavs won three of four against Toronto. So did the man who saw the majority of Jefferson’s minutes in the first round, Iman Shumpert.

Offense Defense Games Matchup Time  Points FGM FGA FG% FTA Drives Drive Points
DeMar DeRozan J.R. Smith 2 9:08 10 4 9 44.4 2 6 4
DeMar DeRozan Richard Jefferson 3 7:43 18 6 15 40.0 8 9 5
DeMar DeRozan LeBron James 3 6:47 2 1 6 16.7 0 3 2
DeMar DeRozan Iman Shumpert 3 4:51 2 1 10 10.0 0 4 2
DeMar DeRozan Tristan Thompson 3 3:07 8 4 10 40.0 0 6 4
DeMar DeRozan Kevin Love 3 3:02 14 7 12 58.3 0 9 4
DeMar DeRozan Kyrie Irving 3 2:34 9 2 6 33.3 6 4 2

Considering Cleveland has yet to see a Milwaukee team with Middleton, and hasn’t faced the full-strength current iteration of the Raptors – Serge Ibaka and P.J. Tucker weren’t around for the Cavs’ three victories – the second round may be the toughest obstacle for James and Co. on the path to a third straight Finals.

Golden State’s biggest test isn’t likely to come until the conference finals. Both the Jazz and Clippers are dealing with key injuries, but looking down the road to a matchup with Houston’s high-octane 3-point variance or the Spurs’ balanced machine reveals a more complicated mission.

After combining for 140 wins last season, the Spurs and Warriors never did get to meet in the playoffs. That might change in Round 3 this time and, if it does, the league’s two best defenses will be on full display.

In two games against Golden State, Kawhi Leonard guarded Draymond Green for a span of 6 1/2 minutes, holding him to a mere two points.

Offense Defense Games Matchup Time Points FGM FGA FG% FTA Drives Drive Points
Draymond Green Kawhi Leonard 2 6:26 2 1 4 25.0 0 4 0
Stephen Curry Tony Parker 2 6:02 8 3 5 60.0 0 1 0
Klay Thompson Danny Green 1 4:54 5 2 2 100.0 0 0 0
Kevin Durant Kyle Anderson 1 3:03 11 4 4 100.0 2 1 2

Draymond dominated on the other end of the floor, however, allowing LaMarcus Aldridge to scored just 11 points in 17 minutes of matchup time. Likewise, Andre Iguodala held Kawhi to eight points in 9 1/2 minutes.

Offense Defense Games Matchup Time Points FGM FGA FG% FTA Drives Drive Points
LaMarcus Aldridge Draymond Green 2 17:29 11 4 10 40.0 2 0 0
Kawhi Leonard Andre Iguodala 2 9:24 8 3 6 50.0 2 3 0
Tony Parker Stephen Curry 2 8:18 9 4 5 80.0 0 5 2
Pau Gasol Zaza Pachulia 3 7:34 7 2 4 50.0 2 1 0

It’s hard to read too much into those numbers considering of the three meetings, the only one in which each team had a fully functional roster took place on opening night in October – a 29-point Spurs win at Oracle Arena. It took five months, but the Warriors got revenge in San Antonio, coming from 22 down to beat the Spurs 110-98 on March 29 even without Kevin Durant.

Vegas considers a Warriors-Cavs rubber match a near certainty by exact matchup standards, giving it a 59 percent chance of happening after both teams’ opening sweeps. There are still some stars that need to align, but it looks even more probable now than it did prior to the playoffs.

It’s certainly more likely than a team blowing a 3-1 lead in the Finals again.

Photos By: AP Photo/Ben Margot/Darron Cummings

Why the Final Four Shouldn’t Be Such a Shock

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The NCAA Tournament is always full of surprises, and this year has been no exception. However, even with two No. 1 seeds and one No. 3 seed in the Final Four, this round of March Madness has somehow proven to be one of the most unpredictable.

Per ESPN’s Tournament Challenge, only 0.003 percent of submissions correctly guessed the  Final Four. Last year, three times that fraction of brackets were picked correctly even though there was a No. 10 seed (Syracuse) crashing the party.

Year Seeds % Correct
2017 (1) North Carolina, (1) Gonzaga, (3) Oregon, (7) South Carolina 0.003%
2016 (1) North Carolina, (2) Villanova, (2) Oklahoma, (10) Syracuse 0.009%
2015 (1) Kentucky, (1) Wisconsin, (1) Duke, (7) Michigan State 1.360%
2014 (1) Florida, (2) Wisconsin, (7) Connecticut, (8) Kentucky 0.006%
2013 (1) Louisville, (4) Michigan, (4) Syracuse, (9) Wichita State 0%
2012 (1) Kentucky, (2) Ohio State, (2) Kansas, (4) Louisville 0.220%
2011 (3) Connecticut, (4) Kentucky, (8) Butler, (11) VCU 0%

In 2014, there was a No. 7 seed and a No. 8 seed in the Final Four, and 0.006 percent (double this year’s share) of people perfected their selections. Why is this combination such a surprise?

The answer is all in the brand. As of late, the two years that produced zero flawless Final Four predictions were the two years that included mid-majors (Wichita State in 2013, Butler in 2011).  Few had even heard of Wichita State (no, Wichita is still not a state) much less picked them to make that sort of run – remember, the Shockers’ 34-0 regular season wasn’t until the year after they made the Final Four. And that 2013 roster wound up having three future NBA players.

Butler was a slightly different story. The Bulldogs had already made it to the national championship game in 2010 and headed into the 2011 tourney having won nine straight. Yet Brad Stevens’ team wasn’t even favored to make it past Old Dominion in the first round, let alone be a member of the Final Four. Hardly a household name to fans then, even if it’s become one since.

Still, these projections somewhat made sense on paper, given that Wichita State and Butler both would have to beat a No. 1 seed to even make the Sweet 16. Going into this year’s tournament, these were the chances of each remaining team making it to the Final Four, according to FiveThirtyEight:

Team % Chance
Gonzaga (1) 41.5%
North Carolina (1) 29.9%
Oregon (3) 6.6%
South Carolina (7) 1.1%

Gonzaga had the highest probability in the entire tournament pool of making it to the Final Four, yet just 37 percent of brackets put them there.

Back to brand. Especially when money is on the line, people are most comfortable choosing teams that have an established name. In other words, people are most comfortable choosing teams that have high seedings, flashy players, and measurable amounts of experience, regardless of anything else that may be relevant.

North Carolina checks all the criteria on that list. It leads the NCAA in Final Four appearances (20) and ranks second in total tournament appearances (48). Think about it. UNC has made it to the Final Four in over 40 percent of its total tournament trips. So, not only did the Tar Heels enter as a No. 1 seed, but one could say that, well … they’ve been here before. Throw in likely lottery pick Justin Jackson, who has averaged 19.8 points, 6.3 rebounds, and 4.3 assists through the Elite Eight, and it’s no surprise that 45 percent of brackets picked UNC to make it this far.

Gonzaga, on the other hand, has been in the Big Dance 20 times and has never played in a single Final Four game. But they received a No. 1 seed for a reason. The Zags suffered just one loss in the regular season, posting a points per game differential of plus-23.4 – the best in Division I since Duke’s 1998-99 juggernaut that featured five lottery picks. The next best this season was Wichita State at 19.6.

Point differential matters here. To win 30-plus games in a season is no easy task, but to win them by that much is truly historic. Gonzaga’s schedule was no cakewalk, either, containing fellow NCAA Tournament teams in Iowa State, Florida, Arizona and Saint Mary’s (three times). Still, people chose brand, so more trust was put into teams like Duke (40%), Arizona (45%), Villanova (48%), and Kansas (58%, the most commonly selected Final Four team via CBS).

Just 9 percent of brackets placed Oregon in their Final Four, compared to 27 percent in favor of fellow-No. 3-seed UCLA despite the Ducks finishing ahead of the Bruins in the Pac-12. Part of that could have had to do with the season-ending injury to big man Chris Boucher, but there are perhaps two bigger reasons. One, UCLA has 48 tournament appearances to Oregon’s 15, including 18 Final Fours – all of which came well after the Ducks’ lone previous trip, in 1939. And second, Lonzo Ball.

We can’t forget South Carolina, which is easily the biggest reason for such bracket mayhem. Sure, they’re a No. 7 seed, they have zero Final Four experience and don’t have a single player who’s a surefire first-round pick. Sure, they had a one percent chance of making it to the Final Four. Sure, just 0.2 percent of nearly 19 million people picked them to make it this far. But should we be this surprised that they did?

The Gamecocks had regular-season wins over Michigan, Syracuse and Florida and has a top-10 KenPom defense that’s one of the most aggressive in the country. They have the SEC player of the year in Sindarius Thornwell. And they have a coach with this perspective (if you have the time, listen to the full 8-minute interview – it’s worth your while).

Maybe it’s not such a surprise. Maybe it’s time that “brand” gets re-branded.

Photos By: AP Photo/Charlie Riedel/Young Kwak/Julio Cortez/Gerry Broome

Impact Percentage: A New NBA Fingerprint

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No two teams in the NBA are alike when it comes to the mixture of player tendencies, lineup combinations and styles of play. One metric that tells a story of player involvement (and furthermore, can serve as a team’s fingerprint) is usage percentage. This estimates the portion of team plays used by a player while he was on the floor. In other words, it shows us how often a player ends his team’s possessions. Players like Russell Westbrook, DeMarcus Cousins, and DeMar DeRozan currently lead the league in usage percentage.

Using our revolutionary STATS SportVU data, we have recently developed an improvement upon traditional usage percentage. Now, not only can we tell how frequently a player terminates a possession (with a FGA, FTA, or Turnover), but we can also quantify how often they impact a possession (with a drive, a ball screen, an isolation or a post up). We’ve appropriately named this value impact percentage.

Why is this valuable? Consider players like Goran Dragic and Mike Conley, who rank 38th and 49th, respectively, in usage. Although a glance at these numbers might persuade one to think that these two aren’t chief possession-influencers, they are both among the top 10 in the league in impact percentage.

This new statistic was mentioned in ESPN the Magazine’s newest analytics issue, which hit the stands on March 17. Today, we are going to use it to analyze Monday’s matchup between Oklahoma City and Golden State.

As mentioned earlier, Russell Westbrook leads the NBA in usage percentage – but he also sits at the top of the leaderboard in impact percentage. When you look at OKC exclusively, no one comes close to the amount of impact that Westbrook has on their possessions.

ImpactPercentage

Usage plays a part here, but when you break down OKC’s SportVU plays – drives, isolations, post ups, and ball screens – it’s evident that these are where the bulk of Westbrook’s impact lies. Westbrook accounts for over 51 impactful plays per game on average. The next closest Thunder player is Victor Oladipo at 16.

ImpactPlays_OKC

Let’s talk about variability. In statistics, standard deviation measures how spread out a distribution is. A low standard deviation tells us that most of the numbers in a sample are close to the sample’s average – in other words, there isn’t much spread. A high standard deviation tells us that the numbers are more scattered. Unsurprisingly, due to Westbrook’s outlandish numbers, OKC has a very high standard deviation when considering impact percentage.

The Thunder’s standard deviation comes to a whopping 15 percent. In comparison, Golden State, before Kevin Durant’s MCL sprain on February 28th, had a standard deviation of 8 percent.

ImpactPercentage_standard-deviation

The biggest and most obvious takeaway here is that these are two completely diverse teams when it comes to how the ball is facilitated, and further, who the offense revolves around. Yet, a deeper dive into the Warriors’ Impact numbers reveals that they’ve had to adjust their strategy in KD’s recent absence.

ImpactPercentage_KD-Table

Steph Curry’s impact percentage has increased from an already team-leading 49 percent to an even higher 55 percent while Klay Thompson’s has shot from 31 to 37. This is all expected. What’s more compelling, though, is how a guy like Ian Clark is doing exponentially more with his limited minutes.

In his first eight games* since Durant’s injury, Clark has nearly doubled his ball screen usage (2.6 to 4.8 per game) and increased his drives (1.2 to 1.9 per game). On top of that, his minutes slightly decreased – even with him playing 34 minutes March 11 against the Spurs as Steve Kerr rested all his stars.

It’s important to mention that impact percentage is not necessarily a reflection of efficiency. Just because a player is influencing his on-court possessions doesn’t mean that he’s influencing them positively. It’s no secret that the Warriors are struggling to fill Durant’s shoes, especially when it comes to productivity – but it does appear as if guys are at least stepping up to the challenge and getting involved.

With all of this in mind, it’ll be interesting to see how OKC handles a Golden State team that finally seems to be adjusting to Durant’s absence.

*all numbers are as of 3/17/17

Photo By: AP Photo/Alonzo Adams
Illustration By: STATS/Andrew Skweres

STATS March Madness 2017 Primer

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East

The Favorite: Villanova (Seed: 1, Final 4 Probability: 29.5%)

Last years’ champs have a pretty tough road ahead of them in the East bracket but obviously have the best chance at advancing to the Final 4 out of the group. Wisconsin in the 2nd round is clearly a bump in the hypothetical road – Wisconsin is STATS #21 team and easily the best 8 seed. We rank Villanova as the #1 team in the nation, though, and we expect them to pass that early test.

Darkhorse:  SMU (Seed: 6, Final 4 Probability: 7.2%)

SMU is a team that can shake up the region. The AAC’s best team is led by the conference’s player of the year, Semi Ojeleye (19.9 ppg on 49.1% FG), and features several other versatile and productive players. The Mustangs are a tougher test for Baylor, Duke, and Villanova (if they happen to make it that far) than people realize. One of those teams could very well be upset, too, which would open up an easier road for SMU to reach the Final 4. There’s a 7.2% chance of that happening – the Elite 8 is a more realistic ceiling.

Bracket Buster: Baylor (Seed: 3, Final 4 Probability: 9.6%)

It’s possible that too many people will have the Bears (who were ranked #1 In the AP Poll at one point this season) going a bit too far. If they get past the dangerous New Mexico State and into the Round of 32, they face a tough path through SMU and potentially Duke, which caps their upside.

Cinderella Team: New Mexico State (Seed: 14, Final 4 Probability: 0.0%)

The top teams in this region are so good that it’s tough to identify any Cinderella candidates. Instead of looking for a Final 4 contender, we look for a team that can a game or 2, and of all the top teams, Baylor is the most vulnerable. New Mexico State beat the only major conference team it faced this year (Arizona State), and they very well could keep it close against a Baylor team that lost to Yale in the 1st round last year. We give them a 13.1% chance of winning that game – very nice for a 14 seed.

West

The Favorite: Gonzaga (Seed: 1, Final 4 Probability: 35.4%)

This could be the year the Zags break through and win a National Championship. Being a #1 seed helps set up an easier path for them, but they will surely be tested as soon as the Sweet 16 against West Virginia (our highest ranked 4 seed). If you don’t think Gonzaga can get it done, look at 2 seed Arizona, who have a 23.4% chance of making it out of the region.

Darkhorse: West Virginia (Seed: 4, Final 4 Probability: 16.4%)

They can certainly knock Gonzaga off in what will be closer to a coin flip matchup than people realize. We see the Mountaineers as a top 10 team, with impressive wins over Virginia, Baylor, Kansas, and Iowa State this season. Potential 2nd round opponent Notre Dame can certainly beat anybody (as proven by wins over Florida State and Virginia), but we still see them as one of the weaker 5 seeds, looking at their overall body of work.

Bracket Buster: Gonzaga

If there were a #1 seed to strategically fade this year it might just be Gonzaga. Unexpected and crazy things will happen this year, just as they do every year. Don’t be surprised if they’re knocked out in the Sweet 16 or Elite 8, as their bracket is stacked with one of the toughest 2/3/4 trios, not to mention 7-seed Saint Mary’s, who beat Gonzaga twice last season. This may just be the quadrant of the bracket that gets flipped on its head.

Cinderella Team: Florida Gulf Coast (Seed: 14, Final 4 Probability: 0.0%)

Dunk City has one thing going for them that no other 14 seeds do: a relatively neutral 1st round location. That’s the silver lining with drawing Florida State, a contender ripe with NBA talent, as the game will be played in Orlando, just about a 3 hour drive from campus. If FGCU does win that game – and we think they have a 13.1% chance to do so – they’ll have some home court advantage against the winner of the underwhelming Xavier/Maryland matchup. These guys could very plausibly make another run to the Sweet 16.

South

The Favorite: North Carolina (Seed: 1, Final 4 Probability: 35.3%)

They’re not only the clear favorite to win the South Region but also a sneaky pick to win it all this year. Why is it sneaky? They aren’t getting as much attention as conference tournament winners, yet we see them as the mathematical favorites. The Tar Heels have a high floor, with the easiest path to the elite 8 – only the bottom half of their region is stacked, meaning they will only have to just face just one top team on its way to the Final 4.

Darkhorse: Wichita State (Seed: 10, Final 4 Probability: 4.2%)

No double digit seed has a better chance of making a run than the Shockers. STATS’ #20 team in the nation should be a much higher seed, and they’re heavy favorites (72.3%) in their 1st round matchup against Dayton. They started slow this year after losing Fred Van Vleet and Ron Baker to the NBA, but then they steamrolled through the Missouri Valley Conference, winning their final 15 games by an average of 22.3 points. The fact they still have decent Final 4 odds despite drawing (potentially) Kentucky, UCLA, and UNC, is a testament to how good this 10 seed is.

Bracket Buster: Kentucky (Seed: 2, Final 4 Probability: 24.7%)

Of course we see them as title contenders, and if they get to the elite 8, they’re a virtual coin toss against UNC. This is about value and perception, though – people may just automatically slot them into the Elite 8 simply because they are the #2 seed, which is a very risky proposition. They may have to face the best #10 seed in tourney history in Wichita State in the Round of 32. After that, they would likely have to face UCLA (another team much better than their #3 seed indicates). They may be a good team to pick to lose early, simply considering their tough path.

Cinderella Team: Middle Tennessee State (Seed: 12, Final 4 Probability: 0.9%)

It’s not often that a 12-seed has anywhere near a 1% chance of making the final 4, but MTSU isn’t your average 12 – they are more like a 10 seed, as STATS’ #41 team in the tournament. It also helps that their 5-seed opponent is relatively weak, with Minnesota ranking as STATS’ #31 team. It all adds up to a very winnable game – MTSU has a 44.6% chance of advancing. Beware, though – the public may be all over the Conference USA champs and winners of 20 of their last 21, but they’re still more likely than not to lose in the 1st round, so Minnesota actually poses the better value.

Mid West

The Favorite: Kansas (Seed: 1, Final 4 Probability: 35.9%)

The Jayhawks just might have the toughest path to the Final 4 of all the #1 seeds. Having said that, they are still the clear favorites and the best bet to come out of the Region on top. Don’t be overly turned off by their Big 12 tourney loss to TCU, as they were missing star freshman and future top 5 pick Josh Jackson for that one, due to suspension.

Darkhorse: Oklahoma State (Seed: 10, Final 4 Probability: 2.7%)

The top 3 seeds here are so good, and they all have 15%+ probabilities of making it to the Final 4. We like the value Oklahoma State presents, though, as the public will be all over their opponent, Big 10 tourney champs Michigan. Michigan is a dangerous 7 seed capable of winning at least a couple games, but Oklahoma State is a good team in its own right, and we see that game as a virtual toss-up. After losing their first 6 Big 12 games, they won 10 of their next 14 games overall, including wins over West Virginia and TCU (twice). The backcourt trio of Jawun Evans (19.0 ppg), Jeffrey Carroll (17.4 ppg), and Phil Forte III (13.3 ppg), are all capable of lighting it up, and they could overwhelm Michigan in the 1st round and break Louisville’s press in the 2nd round.

Bracket Buster: Louisville (Seed: 2, Final 4 Probability: 21.0%)

After their cupcake first round matchup – the Cardinals are likely to face stiff tests the rest of the way. Michigan is one of the best 7 seeds, Oklahoma State is one of the best 10 seeds, and Oregon is one of the best 3 seeds. This is likely an ideal #2 team to short in order to try to get ahead of the field, in case they can’t even reach the Sweet 16. Of all 2-seeds, we give them the lowest probability of reaching the Final 4.

Cinderella Team: Vermont (Seed: 13, Final 4 Probability: 0.2%)

We swear this one all comes down to the numbers and is not influenced by Vermont’s magical win over 4-seed Syracuse in 2005 (“T.J. Sorrentine hit that one from the PARKING LOT!” –Gus Johnson). This version of Vermont is also very good, coming in with the nation’s longest winning streak (21 games). They’re deep and balanced (10 players who average at least 10 minutes; 7 players who average at least 6 points), and they hung in there against Butler in December, losing by 12. No team seeded 13 or higher has a better chance of winning a game than Vermont’s 22.0%.

Make sense of the madness

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March Madness® is one of the highlights of the sporting year. A breathless blur of high-octane basketball, the NCAA tournament captures the attention of the nation as the NBA stars of the future take to the hardwood.

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