Tough Shots No Problem for Cavs’ Smith in NBA Finals


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 or on Twitter at @BHuston_STATS.

Believe Anything By Impactful Players? Look At BABIP Before Thinking PEDs


When a player is performing well above expectations based on past results late in his career, one question still sadly comes to some baseball fans’ minds: Will someone please test that man for performance-enhancing drugs?

That was certainly the case after Eric Thames lit up the majors upon his return from a three-year stint in Korea in which he had a combined .349 batting average and hit 41 home runs per season. The first baseman had only batted .250 with a total of 21 home runs over 181 games with the Blue Jays and Mariners from 2011-12. But the steroid speculation ran rampant after Thames seemed to come out of nowhere (well, really Changwon) to put up a .333 average with 13 homers in his first 32 games with Milwaukee this season.

Apparently the league office was skeptical as well, as according to Yahoo Sports, Thames was tested immediately after a five-game homer streak and a four-gamer in April. The second MLB drug test during that stretch was what prompted Thames’ defiant “I have lots of blood and urine” response. He was reportedly tested for the fifth time after snapping a 15-game homerless streak with a first-inning shot off Mets right-hander Jacob deGrom on May 31, leaving Thames to wonder if MLB’s random drug testing is actually that at all.

That brings us to first baseman Ryan Zimmerman, who is hardly an unknown after spending the past 12 years as a staple in the Nationals’ infield. Zimmerman appeared to be in the twilight of his career and possibly even headed out of Washington in 2016 after posting a career-worst -1.5 BatWAR (batting wins above replacement), which measures a player’s contributions to his team at the plate. That means he was actually costing the Nationals wins when he was in the lineup, the third straight year that number had dropped.

The veteran, however, has experienced an eye-opening rebirth at age 32. Through June 7, he owns a 2.4 BatWAR, has a major league-best .362 batting average and is tied for the NL lead in home runs (17) after hitting a total of 36 over his previous three seasons. Zimmerman also ranks second in MLB with a .459 weighted on-base average, which combines all the different aspects of hitting into one metric and weighs each of them in proportion to their actual run value. For good measure, he’s third with a career-high 185.2 OPS+, which adjusts for league and park factors.

It’s important to note that, by all accounts, Zimmerman hasn’t done much to change his approach this season, which brings us back to the question raised at the start. Oddly enough, Zimmerman was cleared of any sinister activity by Major League Baseball in August, months after a pharmaceutical dealer named Charlie Sly claimed in an Al Jazeera America documentary that Zimmerman used PEDs.

We’d like to believe such occurrences have nothing to do with drugs, but rather the variety of factors that can contribute to any player’s surprising stretch. Batting average on balls in play can provide an indication of how much a player is performing above the norm. Typically, anything north of a .300 BABIP is considered above average, though defensive positioning, luck and how hard a ball is hit can affect that number.

Zimmerman, for example, has a .392 BABIP that ranks sixth in the majors and gives us an area in which to dig deeper. He’s also sixth in the majors in line-drive percentage (30.9) and 18th in average exit velocity (92.9), according to, so he is hitting the ball hard. However, he’s obviously had some luck since he has never finished a full season with a BABIP greater than .334. Zimmerman is expected to be among those who will come back to Earth as his BABIP number almost certainly figures to dip over the rest of the season.

The BABIP leaderboard features many young players having breakout seasons. Minnesota’s Miguel Sano isn’t likely to break the 122-year BABIP record of .443, which was set by Jesse Burkett of Cleveland, and is due some regression after finishing with .396 and .329 marks in his first two seasons. However, he does have a better chance than most to keep a high BABIP because of his 98.8 average exit velocity – tops in all of baseball. Similarly, Aaron Judge of the Yankees isn’t expected to maintain his BABIP but may be able to avoid a severe drop as he ranks second in the bigs with a 96.3 average exit velocity that includes the two hardest-hit balls (119.4, 119 mph) so far this season.

Avisail Garcia of the White Sox is an obvious candidate to fall back as he ranked fourth in the majors with a .392 BABIP. Garcia may have been on his last opportunity in Chicago after posting a .311 BABIP while hitting a combined .250 with 32 home runs over his previous three seasons. He seems to be using an even more aggressive approach than usual as he’s swung on the first pitch an MLB-high 47.5 percent of the time and has missed on just 28.8 percent of his swings overall. Both marks are his best numbers since playing in just 23 games in his rookie 2012 season as a highly regarded prospect with the Tigers.

One might notice that the aforementioned Thames isn’t on the BABIP leaderboard. In fact, the Brewers slugger only has a .303 BABIP that’s right around the typical league average. Because of this, he’s more likely to stay on his current production path than most of the BABIP leaders — no matter how much blood and urine the league office may take from him.

2017 NBA Finals Preview


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.

Three Reasons Why Chelsea Won the 16/17 EPL Season


In the 82nd minute of their 36th game of the EPL season, Michy Batshuayi’s goal secured Chelsea’s fifth English Premier League title. Even though Chelsea won the league with relative ease, it wasn’t all roses from the start. After the first six games, Chelsea had tallied only 10 points. Things came to a head when Chelsea was easily beaten 3-0 by red-hot Arsenal in late September. After that loss, Conte went from a back 4 to a back 3 that served him well at Juventus, and the results improved immediately with Chelsea reeling off 13 straight wins to put them on firm course to win the league.

With the new tools STATS have developed using machine learning, we give three reasons on how Chelsea won the league.

Reason No. 1: Chelsea were incredibly effective in converting chances

Although Chelsea scored the most goals this season, they only ranked fifth in the league in terms of chances created (see Figure 1). To estimate the number of chances created, we use the expected goals (xG) measure, which estimates the likelihood that the average league player will score a goal based on the situation (i.e., ball position, game-context etc. – see [1] for more details). 


Figure 1: Plot showing how many goals each team could have expected to score given the situation (Chelsea rank 5th with approximately 60 goals expected).

However, what highlights their offensive effectiveness in this year’s EPL is their xG plus-minus (xGpm), which is +22.4, meaning that Chelsea scored +22 more goals this season than expected[1].  To put this +22.4 measure in context, when we compare Chelsea with other teams this year, we see that they are executing their chances in a much more clinical fashion (see Figure 2). Tottenham are the next-closest team in terms of plus-minus with a +15.4 (although with two games remaining the Spurs were only +7.6 – meaning the last two games where the scored 13 goals somewhat inflated this statistic), followed by Liverpool (+4.8), Bournemouth (+4.6) and Burnley (+3.1). Southamption, on the other hand, were quite the opposite, missing more than 16 goals that the average team would have converted.


Figure 2: Ranking the teams on their goals-expected goals in the 16-17 EPL. Chelsea have a +22.4, seven more than Spurs.

From a historical perspective in terms of how this team compares in xGpm across the last six seasons in which we have calculated this statistic, we see that this Chelsea team are ranked third, with only Liverpool and Manchester City in the prolific 13-14 season being more effective (see Table 1).

Table 1: Ranked list of the most effective offensive teams across the last 6 seasons.

Rank Season Team xGpm
1 16-17 Chelsea +22.4
2 13-14 Liverpool +22.0
3 13-14 Manchester City +19.1
4 16-17 Tottenham +15.4
5 13-14 Arsenal +13.4

Needless to say, scoring 22 goals more than expected goes a long way to securing a title. However, as we will see in the next section, their defense played a massive role as well.

Reason No. 2: Defensively, Chelsea did not give up many chances

Similarly to what we did in the previous section, we can use the expected goals measure to analyze how effective a team’s defense is. Although Chelsea ranked fifth in creating chances, they are first defensively (see Figure 3).


Figure 3: Expected goals against measure which estimates how many goals a team should have conceded based on game situation. Chelsea gave up the fewest chances.

In terms of goals conceded, it is clear that Tottenham were far superior in terms of defense (26 vs 33). But when we look at the expected save (xS) measure, which estimates the likelihood that a shot will end up as a goal based on the player’s position and shot location, we can see that Hugo Lloris saved more than 10 goals that the “average league goalkeeper” would not have. Chelsea’s goalkeeper performance this season, on the other hand, was -2.  Figure 4 shows how the goalkeepers fared based on goals conceded minus the expected save value.


Figure 4: Comparing goalkeeping performance this year based on Saves vs Expected Saves

Reason No. 3: Chelsea went to a back 3 to provide more defensive stability

In the previous two sections, we showed quantitatively how Chelsea fared both offensively and defensively in terms of goal-scoring chances. But as noted earlier, after six games and a poor run of form, Antonio Conte changed from a back 4 to a back 3 – a move that’s been hailed as a key decision in turning things around. In this section, we show how the change in formation changed their style of play.

To do this analysis, we compared the performances of Chelsea for the first six games (until the Arsenal vs Chelsea match on Sept. 24) to the performances after. A summary of some key performance metrics are shown in Table 2. From this table, it can be seen that although Chelsea averaged more shots with a back 4 (16.8 vs 14.1 per game), they actually averaged more goals with a back 3 (2.2 vs 1.7). Defensively, they conceded the same amount of shots, but with a back 3 they conceded far fewer goals per match (0.7 vs 1.5). In terms of possession, with a back 3 they actually gave up around 4% possession per game, which indicates a change in playing style.

Table 2: Comparing offensive and defensive metrics when Chelsea had a Back-4 and Back-3.

Measure Offensive Defensive
  Back 4 Back 3 Back 4 Back 3
Shots per game 16.8 14.1 8.5 8.6
Goals per game 1.7 2.2 1.5 0.7
xG per game 1.6 1.5 0.9 0.7
Possession per game 57.3% 53.3% 42.7% 46.7%

Using a new metric developed at STATS, we can break up all continuous play possession into a series of “style” states, which automatically assigns a portion of a game into one of these distinct game phases. These style names are quite self-descriptive (i.e., direct-play, counter-attack, maintenance, build-up, sustained-threat, fast-tempo, crossing, high-press – but for more details see [2]).

In Figure 5, we compare Chelsea’s playing style between when they played with a back 3 and a back 4. From viewing this plot, it can be seen that when Chelsea played with a back 3 they used a lot more direct-play and their use of maintenance, build-up and sustained threat reduced. With a back 3, they also utilized less crosses. In terms of goal-scoring efficiency, this makes sense as it has been shown previously that the most effective way of scoring is via direct-play [3].


Figure 5: Chart comparing Chelsea’s playing style with a back-3 (blue) compared to back-4.

In Figure 6, we show the defensive playing style of Chelsea (i.e., how opposition teams tend to attack when they have possession of the ball).  What is interesting to note is that we have the opposite occurring, with Chelsea having less direct-play and more maintenance and crossing against them. As having a back 3 is thought to give a team more “defensive stability,” it also correlates with Chelsea conceding fewer good chances.


Figure 6: The defensive playing style of Chelsea (i.e,. when teams have the possession of the ball against Chelsea).


Using new analysis tools developed at STATS, we have been able to objectively measure Chelsea’s title run using expected goals, expected saves and playing styles measures.

[1] In our analysis, we have classified own goals down to luck, so in determining the “expected goals plus-minus” (xGpm) we exclude own goals from the goals values (i.e., xGpm = (Goals – Own goals) – xG.

“Conte vs Mourinho:” Comparing the Chelsea Playing Styles of Champions


Much has been made of the impact and difference of Chelsea’s playing style since the arrival of Antonio Conte. Seeing that most of the squad that won the title in 2014-15 is on the current 2016-17 squad, this leads us to the question: “what is the difference between the two?” Specifically, how is the style between the two teams different?

With the new tools STATS has developed using machine learning, we can see where the teams are similar and where they are different. In this article, we run through a playing style “checklist,” which enables us to have a better understanding of how they have achieved success in these respective seasons. Overall, it will give us a sense whether there is a distinct change in playing style under Conte, whether they are the same – or somewhere in between.

Comparison No. 1: Creating and Executing Chances

The first thing to compare is how many chances each team created, and how effective they were in executing those chances (see Table 1 for summary). In the 14-15 season, Chelsea created around 15.1 shots per game, which is slightly above what they are achieving this year (14.6) but not significant.

What is staggering though, is how effective they have been in converting chances this year. The expected goals (xG) measure is a tool we can use which estimates the likelihood that the average league player will score a goal based on the situation (i.e., ball position, game-context etc. – see [1] for more details).  This year, across 35 games, Chelsea have scored 75 goals but have an expected goal value of 54. Given that one of these goals were from two own-goals, their xG plus-minus (xGpm) is +19, meaning that Chelsea have scored +19 more goals this season than expected[1]. This compares to the 14-15 season, when Chelsea had a xGpm of +8.5 and scored 73 goals but were expected to score 63.5 (one of those was an own goal as well).

Table 1: Comparing the chance creating and execution between the 14-15 and 16-17 Chelsea squads.

Season Games Shots/(shots per game) Goals/ (goals per game) Own goals Avg chance xG xGpm
14-15 38 573 (15.1) 73 (1.9) 1 11.1% 63.5 (1.7) 8.5
16-17 35 510 (14.6) 75 (2.1) 2 10.5% 54.0 (1.5) 19.0

In terms of explaining why Chelsea are more effective this season compared to the 14-15 version, let’s look at the individual contribution of the top scorers for the respective seasons (see Table 2). In the 14-15 season, Diego Costa was the leading goal scorer, netting 20 goals from an expected value of 14.8 – meaning he was +5.2 better than the average striker that year. The next-leading scorer was Edin Hazard, who scored 14 goals, with a plus-minus of +1.2.

Fast forward to this season, where Costa has been the leading scorer with 20 goals so far. However, his plus-minus has only been +2.4. Hazard has also been a leading light, scoring 15 but with a plus-minus of +4.5. Probably the key difference between this year and other years, has been the contribution of the other players – not in terms of the number of goals they have scored but by how efficient they have been. Pedro and Willian have been excellent – and in terms of their effectiveness in front of goal, they have been quite clinical with plus-minuses of +2.6 and +4.6, respectively. Even Marcos Alonso has been effective from his customary left-wing-back position, chiming in with six goals, with a plus-minus of +2.5.

A thought of Jose Mourinho teams in the past has been their reliance on individual brilliance instead of focusing on offensive team play. This table suggests that Conte has been able to extract more from other key offensive players – not just Costa and Hazard. This begs the question: Do Chelsea create chances in a different manner from Mourniho’s 14-15 team, or are they just better finishing the chances?

Table 2: Table comparing goals-scorers of the 14-15 season and the 16-17.

2014-2015   2016-2017
Player Goals xG G-xG Player Goals xG G-xG
COSTA 20 14.8 5.2 COSTA 20 17.6 2.4
HAZARD 14 12.8 1.2 HAZARD 15 10.5 4.5
REMY 7 3.7 3.3 PEDRO 8 5.4 2.6
OSCAR 6 5.5 0.5 WILLIAN 7 2.4 4.6
TERRY 5 4.1 0.9 ALONSO 6 3.5 2.5
IVANOVIC 4 4.1 -0.1 CAHILL 6 2.8 3.2
DROGBA 4 2.4 1.6 FABREGAS 4 2.4 1.6
IVANOVIC 4 4.1 -0.1 MOSES 3 2.9 0.1

Comparison No. 2: Is there any difference in how they created chances?

Even though the 14-15 and 16-17 teams created approximately the same amount of chances per match, there could be a difference in how they created the chances. Recently, we have created a dictionary of scoring methods which are described below: Build-Up/Normal, Counter-Attack, Direct-Play, Corner Kick, Free-Kick, From-Free-Kick, Cross, From Cross, Throw-In and Penalties. In Figure 3, we show how many chances were created for each type of shot.


Figure 1: The creation of chances between the 14-15 and 16-17 seasons.

In Figure 3, we can see a number of things:

  • In the 14-15 season, 50.1% of the chances created were in the build-up style, compared to this year, which is 37.7%.
  • This season, Chelsea are creating more chances from direct-play, free-kicks and crosses.
  • There is no difference in terms of chances created for counter-attacks, corner-kicks and penalties.

As Chelsea are +19 in terms of expected goals plus-minus, does this change where the goals are coming from? In Figure 4, we show the comparisons of how they’re scoring. The key points are that Chelsea are getting fewer goals from build-up and corner kicks and more from the counter-attack and direct-play in the 16-17 season.


Figure 2: Where are the goals coming from? Comparison of goals between the 14-15 and 16-17 squads.

Comparison No. 3: Conceding Chances

Now that we have compared the offensive performance between the 14-15 and 16-17 squads, we can do the same on the defensive side. Table 3 gives a summary of both teams. The first thing to notice is that the current squad gives up far fewer shots per game (8.6 vs 11.2). When we look at the expected goal plus-minus, we can see that Chelsea were -4.9, meaning that they should have conceded nearly five more goals then they have (see next comparison on goalkeeping to see a reason why this was the case). This season, their plus-minus is 1, meaning that they have conceded one more goal than what we would expect them to have.

Table 3: Comparing how opposition teams created and executed chances between the 14-15 and 16-17 squads.

Season Games Shots/(shots per game) Goals/ (goals per game) Own goals Avg xG xG xGpm
14-15 38 424 (11.2) 32 (0.8) 1 8.5% 35.9 -4.9
16-17 35 301 (8.6) 29 (0.8) 1 9.1% 27.0 1.0

A strong cue into describing the defensive discrepancy between the two seasons relates to goalkeeping. Using the expected save (xS) value, which estimates the likelihood that a goalkeeper should have saved a shot based on the game situation (i.e., player position, ball position and game-phase). In Table 4, we can see that Thibaut Courtois was more effective in the 14-15 season with a expected-save plus minus of +4.3 compared to this year’s -0.5. This means that he saved four goals more than the league-average keeper would have saved in the 14-15 season, compared to -0.5 goals this year – just below the league average.

Table 4: Comparing goalkeeping performance between the 14-15 and 16-17 squads.

Season Games Saves xSaves Saves-xSaves
14-15 38 91 (2.4) 86.7 4.3
16-17 35 72 (2.0) 72.5 -0.5

Comparison No. 4: Playing Styles

Using a new metric, which we have developed at STATS, we can break up all continuous play possession into a series of “style” states, which automatically assigns a portion of a game into one of these distinct game phases. These style names are quite self-descriptive (i.e., direct-play, counter-attack, maintenance, build-up, sustained-threat, fast-tempo, crossing, high-press – but for more details see [2]).

In Figure 3, we compare Chelsea’s playing style. Generally, they have a similar possession percentage, and a similar amount of direct play. But there is a substantial relative increase in counter attack, from 14.2% to 24.5% compared to the league average. To give this increase some context, Leicester last season showed a +26.2% in counter attack, just slightly above Chelsea this year (and Leicester were considered very counterattack-heavy). Interestingly, Leicester paired that with an above average direct play, whereas Chelsea are well below average in this respect.

In terms of the possession-based styles (i.e., maintenance, build up and sustained threat), while they have a similar amount of possession, they seem to be less offensive when they have long possessions than they were in 14-15. In terms of the other style categories, there was a big increase in fast-tempo this season, which means they had more possessions where they circulate the ball quickly in the opposition’s half. In terms of crossing, this season they were on par with the league average in terms of crossing, whereas before they were quite a bit below the league average (+0.6% this season, -24.1% in 14-15). In terms of high pressing, they do less than in 14-15.


Figure 3: Chart comparing Chelsea’s playing style in 14-15 vs 16-17.

Summary: Chelsea Composite Squad: (Formation 3-4-3)

Based on our analysis and advanced metrics, we have put together a composite starting 11. As the 3-4-3 this year has been more effective defensively, we have used this formation.

Goalkeeper: Thibaut Courtois (14-15)

Back 3: Cesar Azipuleta, David Luis and Gary Cahill (c) (all from the 16-17 squad)

Left Wing-Back: Marcos Alonso (16-17) – due to his goal-scoring feats and as well as the defensive exploits in this 16-17 title winning squad).

Right Wing-Back: Pedro (16-17) – obviously being played out of position and Victor Moses would feel hard done by, but there were some other performances from the 14-15 squad that were impossible to not include.

Holding Midfielders: N’golo Kante, (16-17) and Nemanja Matic (14-15)this year’s PFA player picks himself, but Matic’s imperious form in 14-15 was monumental in helping the Blues win the title.

Forward Three: Cesc Fabregas (14-15), Diego Costa (14-15) and Edin Hazard (16-17) – Fabregas was immense in the 14-15 season (and has played some very important cameos this season) and deserves a spot in the starting 11 (although he played out of position). Diego Costa has been a colossus in both seasons, scoring 20 goals in each – we choose the 14-15 season version as he was slightly more efficient in terms of xG. Similarly, both versions of Edin Hazard would be first name of the team-sheet, but again, due to his xG efficiency this season, the 16-17 version gets the starting birth.

Manager: Antonio Conte – both the 14-15 and 16-17 squads were similar in a lot of their attributes. But in terms of sheer efficiency in both offensive and defensive departments, Conte gets the nod by revamping the squad and by changing formations – which ultimately altered the fortunes of this Chelsea squad.

[1] In our analysis, we have classed own-goals down to luck, so in determining the “expected goals plus-minus” (xGpm) we exclude own goals from the goals values (i.e., xGpm = (Goals – Own goals) – xG.

Applying 3D Modeling to Player Performance


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.

Bringing Design to Performance Analysis


STATS became the leader in sports intelligence by being the pioneer of live sports data. We strive to continue to innovate in the sports industry and our latest effort has focused on the design of our new products. STATS has engaged with our customers and partners in Design Thinking during the creation of our new products to gather instant feedback.

Design Thinking is a methodology used by designers to solve complex problems and find desirable solutions. The design mindset for our products is solution focused and action oriented towards creating a preferred future state. Design Thinking draws upon the logic, imagination, intuition, and systemic reasoning of many users, to explore possibilities of what could be—and to create desired outcomes that will benefit the customer.

For our new football product, launching this summer, we have conducted in-depth interviews with 10 clubs to understand team needs and validate the functionality of the product. We believe that co-creation with customers is key in design thinking – as our partners meet product managers, sales, marketing and design, we are able to journey map pain points and co-create possible ideas and solutions.

Ryan Nasipak, STATS Product Designer, has implemented the Design Thinking process at STATS for our Team Performance products. The iterative method of Design Thinking enables the product teams to accelerate the design process in combination with understanding real use cases from the user’s perspective.

“Design Thinking lets us see how managers and coaches would actually use our product for their match analysis, versus theorizing how they would,” said Nasipak. “Our goal is to develop a product that not only solves problems that all teams face, but also unlocks a new reality for their day to day work. Having our users involved early in that process helps us address their most critical needs and test that our products are properly addressing them. This not only creates a better user experience but happier customers.”

Long Live the Long Ball


Gone are the days of offenses dominating the major leagues. We’ve moved from the high-scoring, PED-fueled era of the early 2000s to a game that’s more focused on power pitching, specialized bullpens and a shift (often quite literally) in defensive intelligence. After scoring reached an all-time high with more than 10 runs per game in 1999 and 2000, it settled well below 9.0 per since 2010 before finishing just a shade under that last year.

We’re back to just 8.48 runs per contest thus far in April, and pitchers are allowing a mere 8.16 hits per nine innings, which is the lowest average before the calendar has flipped to May since 1968 (all statistics through Monday unless otherwise noted). A major league record for strikeouts has been set every year since 2007, and with 21.8 percent of plate appearances ending with a K, that trend is easily on pace to continue as we approach the one-month mark.

Hits at a historic low, strikeouts at an all-time high and runs not exactly crossing the plate with ease. All signs that point to a massive power outage at the plate, right?

Not quite. Not at all, in fact.

Home runs in 2014 were hit as infrequently as they’d been since the mid-90s – 1.44 per game to be exact – but they’ve jumped a staggering amount since then. There were an average of 2.02 long balls in each contest in 2015 and 2.32 last year, which was the second-highest ever behind that historically roid-fueled 2000 season. With 2.26 per game in April, 2017 is well above last year’s 2.10 first-month pace.

2017 13.88
2016 13.26
2000 12.58
2001 12.43
2004 12.24
2006 11.94
2003 11.81
2012 11.73
2002 11.69
2009 11.58

There’s no shortage of theories as to why this is the case. Juiced baseballs? Some aren’t so sure. Smaller ballparks? Perhaps, though until Atlanta’s SunTrust Park opened this month, MLB hadn’t christened a new venue since Marlins Park in 2012 (though that stadium, Citi Field and Petco Park have recently brought their fences in). Something … less natural? Of the 14 major leaguers who have been suspended since 2015, half were pitchers. Only Marlon Byrd, who is currently suspended for 162 games, saw any significant power boost.

One theory is that more teams are aiming for the skies when they step into the batter’s box, so a trend toward swinging for the fences is bound to produce a home run increase. With shifting having exploded and defensive alignments more capable than ever of sucking up ground balls, what’s the point of trying to find a hole that might not exist?

“I know our hitting coach wants you to hit the ball in the air,” Cubs starter Jon Lester told The New York Times last season. “There’s no slug on the ground. Guys are willing to take their punch-outs to hit the ball in the air, and swing hard in case they hit it.”

Lester’s not just paying lip service to John Mallee, who since being put in charge of the Cubs’ hitters in 2015 has overseen a team that’s hit more fly balls than all but four major league teams (and sits in the same spot in terms of strikeouts in that time). While a lot of hitting coaches have been hesitant to bend to an analytical side of the sport few could imagine a decade ago, Mallee isn’t one of them. After just a year on the job, Mallee appeared at an American Baseball Coaches Association convention and delivered a presentation on swing analysis that essentially served as a manifesto to proper launch angle and exit velocity.

Take Jason Heyward. (We’re using him as an example, but that’s a sentence Cubs fans uttered on a daily basis last season in the first year of a deal that will pay him more than the GDP of a number of Pacific island nations until 2023.) His swing was broken in every way possible, leading to a .230 average, seven homers and notoriety as one of the worst offensive regulars in baseball even before factoring in his contract.

Heyward’s 87.2 mph average exit velocity last season was a 3.4 mph drop from his 2015 season in St. Louis, so he went to work with Mallee in the offseason on improving the bat angle and sequence of his swing. A lot of the video Mallee and Heyward studied went back much farther, to 2012, when Heyward hit a career-high 27 dingers with the Braves and seemed to be on the verge of being a 30-homer regular.

Heyward has cut eight to 10 inches off his swing path and moved his hands down the bat, and the results are encouraging. His exit velocity is up to 91.2, and he’s homered three times in his last four games through Monday.

It’s too early to tell if Heyward’s mechanical adjustments will make a long-term difference, just as it seems awfully unlikely the Brewers’ Eric Thames will set the single-season home run record.

But baseball’s most surprising April star is finding his return to life in the bigs awfully hospitable. Thames was a major league washout after spending parts of 2011 and 2012 with Toronto and Seattle, but he became a star in Korea by hitting a combined 124 homers from 2014-16. There aren’t many flamethrowers in the KBO, so Thames had to ramp up his relatively non-existent plate discipline in order to deal with the vast array of breaking pitches he’d see in nearly every at-bat.

“I had to really bear down in the strike zone and learn how to have plate discipline,” Thames told USA Today’s Bob Nightengale. “I would have to carry that here because they throw harder and the strike zone is bigger.”

So far, so good, now that he’s back playing in a league that’s throwing harder than ever. He’s hit 10 homers this month, seven of which have come with two strikes. Thames had as many homers against the Reds alone through Monday as any other player did overall.

Whether Thames ultimately proves to be a flash in the pan remains to be seen, but we can safely say that, barring injury, he’ll at least get to 20 homers. That’s the thing about the modern long ball. We may never see individual stretches of brute force like McGwire, Sosa, Bonds and A-Rod provided, but the distribution of power is wider than ever. In 2015, 64 players hit at least 20 homers. Last season, there were a major league-record 111 players who left the yard 20 times – nine more than that record-setting 2000. Think about that. If we assume there are 235 major league regulars among hitters, we’re getting close to half of those who are threats to hit 20 homers.

There’s no doubt park factors, swing mechanic data leading to better launch angles, and record-setting pitch speeds producing bigger exit velocities have played a role in homers again being on the rise. But more than anything, batters are trying to go deep as a simple survival skill. If there’s not enough pop from a certain position, there might be another Thames out there who can change the game with one swing.

Also, chicks dig the long ball.

Analyzing a Potential NBA Finals Rematch


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


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