Passan: The five biggest takeaways from Statcast’s swing-tracking data
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Jeff Passan, ESPN
Major League Baseball publicly released a trove of bat-tracking data today that offers fascinating insights into what makes the best hitters good — and the worst bad. With everything from bat speed to swing length to sweet spot contact measured, it will have a similarly profound effect on hitters that ball-tracking data had on pitchers.
Using the Hawk-Eye tracking system that positions 12 cameras around every major league stadium — including five running at 300 frames per second — MLB has spent more than two years refining the bat-tracking model before releasing it on its Statcast platform. In measuring using the sweet spot about 6 inches below the head of the bat, every swing of every hitter is documented through objective data and ready for analysis.
Here are the basics. The average major league swing is 71.5 mph. The average length of the bat’s path on a swing, start to finish, is 7.3 feet. Hitters square up the ball on one-third of batted balls. The fastest swings typically belong to the most productive players — but not always. The average bat speed for the best hitter in the major leagues this season, Shohei Ohtani: 75.4 mph. The average bat speed for the worst hitter in the major leagues this season, Javier Baez: 75.4 mph.
Just as the advent of the pitch-tracking era prompted changes in training methods to juice velocity and spin, the ability to measure bat speed and paths will likewise change the approaches of hitters in future years. For now, though, in this nascent stage, the data is pure and unadulterated. And it tells us that when it comes to bat speed, there is one man, and then there is everyone else.
The king of bat speed
When Statcast debuted in 2015 and exit velocity jumped to the fore of baseball lexicon, Giancarlo Stanton, then with the Miami Marlins, topped almost every leaderboard. That season, there were 12 balls hit at least 117 mph. One from Mike Trout, one from Nelson Cruz, one from Carlos Gonzalez and nine from Stanton.
The now-New York Yankees slugger’s bat-speed numbers are similarly gaudy. Stanton’s swing, on average, comes in around 80.6 mph — nearly 3 mph higher than the second-fastest swinger, Pittsburgh Pirates shortstop Oneil Cruz. It’s also consistently fast. Statcast is characterizing all swings over 75 mph as “fast.” Just over 22% of swings reach the 75 mph threshold. Stanton is at 98.0%, nearly 25% ahead of the next best, the Philadelphia Phillies’ Kyle Schwarber, who swings 75-plus mph 73.9% of the time.
Stanton is also near the top of another category: swing length, where he’s second behind Baez. Height often influences swing length, and at 6-foot-6, it’s no surprise to see Stanton’s swing covering 8.4 feet.
Of course, as Stanton’s struggles in recent years have taught, exit velocity — and now, bat speed — do not by themselves make for a great hitter. Stanton has the single hardest-hit ball in MLB this season at 119.9 mph and the highest average exit velocity on his hardest-hit balls, but he has been only a slightly-above-league-average hitter, batting .230/.283/.452.
The lesson: You can have the fastest swing around, but by no means does it guarantee success.
The anti-Stanton
On the other end of the spectrum is San Diego Padres craftsman Luis Arraez, who can add a new title to his two batting crowns: the slowest bat in baseball. Arráez’s bat speed of 62.4 mph lags 2 mph behind the second-most languid, Cleveland Guardians outfielder Steven Kwan, and the two are perhaps the best examples of what players without elite bat speed can do to continue thriving in the big leagues.
Arráez and Kwan are part of the cohort of controlled, short swings that get squared up with a phenomenal amount of regularity. Arráez’s swing is just 5.9 feet and Kwan’s 6.4. In the group of sub-68-mph bat speed and sub-6.4-foot swing length are Milwaukee Brewers second baseman Brice Turang (128 OPS+), Yankees outfielder Alex Verdugo (107) and Toronto Blue Jays DH Justin Turner (111), all of whom are productive offensive players.
One might suggest it’s in spite of their swings, but perhaps it’s better to start treating it like it’s because of them. Arráez leads MLB by squaring up the ball on 43.9% of his swings. To determine whether a pitch has been squared up, the system takes two variables — bat speed and pitch speed — and determines the maximum exit velocity. Then it takes the actual EV on a batted ball and compares it to the peak. If it’s at least 80% of the top-end number, it is deemed to be squared up, because only balls that hit the bat’s sweet spot can produce 80%-plus velocities.
When hitters square up a ball, they bat .372 and slug .659. When they don’t, they hit .127 and slug .144. In other words, even if neither possesses much power, appreciate Arráez, Kwan and others for what they are: masters of the art of hitting.
The perfect marriage of bat speed and precision
Take Stanton, put him into one of those mash-up machines with Arráez, and what do you get?
Juan Soto. Just consider:
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At 76.1 mph, the Yankees right fielder has the 10th-highest bat speed among the 221 qualified players.
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He swings 75-plus mph 66% of the time, ranking seventh.
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He has squared up 83 balls, the fourth most in MLB, and does so at a 48.3% rate, which is second.
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He is second in blasts, a metric that adds an element of bat speed to a player’s squared-up rate, with 49. The top 10 players in blasts are a “who’s who” of great hitters: Jose Ramirez, Julio Rodriguez, Aaron Judge, Yandy Diaz, Gunnar Henderson, Salvador Perez, Bobby Witt Jr., Ohtani, Soto and a surprising No. 1 whom we’ll introduce next.
A swing length of 7.3 feet is the only place where Soto is average. He’s not like Corey Seager, Freddie Freeman and Wyatt Langford, who generate excellent bat speed with short swings. Nor is he like the majority of players who join him near the top of the bat-speed list and generate it using long swings.
No, Soto is just spectacular at what he does. And his outlier status in bat-tracking data validates his place there with production, too.
The best hitter in baseball nobody knows
He has more blasts than Soto and Ohtani.
Only four players have squared up more balls than him, and each is a multitime All-Star.
He doesn’t even swing, on average, as hard as his brother. But that doesn’t matter, because William Contreras — the Brewers’ catcher, younger sibling of St. Louis Cardinals catcher Willson Contreras — does plenty of damage with a 74.2 mph effort. Not only is the 26-year-old Contreras atop the list of blasts, it’s not particularly close: His 58 are ahead of Soto’s 50 and Ohtani’s 46, and his big league-best blast rate of 34.5% is 2½ times the major league average of 13.7%.
The reason for Contreras’ success is clear: He swings hard, hits the ball very hard and doesn’t strike out much (sub-20% punchout rate on the season). It’s an exceptional combination of skills, and to have maintained this offensive output playing every Brewers game, not to mention 33 of 40 at catcher, is MVP-caliber work.
Others this season whose bat skills deserve credit:
Whose profiles are alarming?
While MLB attempted to start tracking swings using Statcast in a limited number of stadiums during the 2022 season, the league only felt confident enough this year to release the full set of numbers. Thus, it’s impossible to know for certain whose swing has gotten faster or slower in recent years.
Here are five players whose swing metrics over the season’s first seven weeks are cause for concern.
Javier Báez, SS, Detroit Tigers: Never has bat speed been a question for Báez, and this season reinforced that. The issue — or one of the issues — is that he lugs his bat through the zone longer than anyone, Stanton included. Baez’s 8.7-foot-long bat path simply doesn’t generate the hard contact it once did, and his .172/.208/.233 line reflects that.
Nolan Arenado, 3B, St. Louis Cardinals: Right behind Baez and Stanton in swing length is the 33-year-old Arenado. Long swings can be a good thing — Michael Harris II, Aaron Judge, Willy Adames, Rhys Hoskins and Adolis Garcia all rank in the top 10 — but they’re tough on a pull-heavy hitter with well-below-average bat speed. Arenado has clocked in at 69.5 mph this season, and while he’s been an average hitter in a down offensive environment, only a few others (Isaac Paredes, Jose Altuve) have found success with long swings and slower bats. All three have low blast rates, which is worth keeping an eye on.
Vladimir Guerrero Jr., 1B, Toronto Blue Jays: The 25-year-old has the makings of a good hitter. An average bat speed of 75.6 mph (14th in MLB) and 34 blasts (22nd) portend well. The issue? Guerrero is squaring up the ball at an anemic rate: just 21% of swings and 26.9% of the time on contact. The blasts show that when Vlad does hit the sweet spot, he does significant damage. He just hits the weak part of the bat far too often.
Jorge Soler, DH, San Francisco Giants: As bad as Guerrero has been at squaring up the ball, Soler is markedly worse. His bad speed is the same as Vlad’s at 75.6 mph, but he has the third-lowest squared-up rate on contact. The blasts are even worse: Soler has been the only player in baseball who swings harder than 73.2 mph and can’t muster even a 10% blast rate. Perhaps the right shoulder strain that forced him to the IL a week ago was the culprit? No longer is that a question left to speculation. The data upon Soler’s return will answer it.
Brett Baty, 3B, New York Mets: At the bottom of the list is Baty, the clearest example of the anomaly that is high bat speed, weak contact. While Baty doesn’t swing as hard as Soler or Guerrero, his 73.2-mph swing is certainly above average. His MLB-worst 18.0% squared-up rate on contact, on the other hand, is not. Getting out-blasted by Arráez when swinging 11 mph harder than him is a difficult thing to do.
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Greg Wyshynski
The Winnipeg Jets defended star goaltender Connor Hellebuyck after another disastrous performance on the road, a 5-2 loss Friday night in which the St. Louis Blues forced Game 7 in their Stanley Cup Playoff opening-round series.
Hellebuyck was pulled after the second period in favor of backup Eric Comrie, the third straight game in St. Louis that he failed to finish. Hellebuyck surrendered five goals on 23 shots, including four goals on eight shots during a 5-minute, 23-second stretch in the second period that cost the Jets the game.
As has become tradition in this series, Blues fans mockingly chanted, “We want Connor!” after Hellebuyck left the game and the Jets’ bench.
“This isn’t about Connor,” Winnipeg coach Scott Arniel said. “Tonight was not about Connor. Tonight, we imploded in front of him. Now, it’s a one-game showdown. It’s our goalie against their goalie, our best players against their best, our grinders against theirs. I have a lot of confidence in our [entire] group — not just Helly.”
Hellebuyck won the Vezina Trophy last season as the NHL’s top goaltender, as voted on by the league’s general managers. He also won the award in 2019-20 and is the favorite to win it for a third time this season. Hellebuyck is also a finalist for the 2024-25 Hart Trophy, awarded to the NHL’s most valuable player. He was the starting goaltender for Team USA at the 4 Nations Face-Off tournament in February and was expected to do the same for the U.S. in next year’s Winter Olympics in Italy.
But his recent performances in the Stanley Cup playoffs have been the antithesis of that success.
Over the past three postseasons, two of which the Jets lost in the first round in just five games, Hellebuyck is 5-11 with an .860 save percentage.
Hellebuyck failed to finish any of the three games played in St. Louis during the series. He was pulled with 9:28 left in regulation in Game 3, having given up six goals on 25 shots. In Game 4, Hellebuyck was pulled 2:01 into the third period after surrendering five goals on 18 shots. Hellebuyck has allowed four or more goals in seven straight road playoff games, tying the second-longest streak in Stanley Cup playoff history
At home against the Blues in this series, it has been a different story, if not necessarily a great one: Hellebuyck is 3-0 in Winnipeg, with an .879 save percentage and a 2.33 goals-against average.
His home numbers in the regular season: 27-3-3 with a .938 save percentage and a 1.63 goals-against average in 33 games. His road numbers: 20-9-0 with a .911 save percentage and a 2.43 goals-against average. Hellebuyck was not pulled in his 63 appearances in the regular season.
Even with Hellebuyck’s multiple seasons of playoff struggles, his team exonerated him from blame for the Game 6 loss.
“I don’t need to talk about Bucky,” said forward Nikolaj Ehlers, who returned to the lineup for the first time since April 12 after a foot injury. “He’s been unbelievable for us all year. He’s continued to do that. We’ve got to be better.”
Said forward Cole Perfetti, who had a goal in Game 6: “Things got carried away. We lost our game for four or five minutes. They got a couple pucks through, and they found the back of the net. It’s frustrating. Happened a couple of times now this series where we fell asleep and they jumped on us.”
Perfetti said the Jets have rebounded from losses like this — one reason their confidence isn’t shaken ahead of Sunday’s Game 7.
“We had a loss like that in Game 4 [in St. Louis],” he said. “We went home and got the job done in Game 5. We’ve got the home ice. We’ve got the fans behind us and our barn rocking.”
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Bill Connelly
Almost no word in the English language makes a college football fan more defensive than the L-word: luck.
We weren’t lucky to have a great turnover margin — our coaches are just really good at emphasizing ball security! We’re tougher than everyone else — that’s why we recovered all those fumbles!
We weren’t lucky to win all those close games — we’re clutch! Our coaches know how to press all the right buttons! Our quarterback is a cool customer!
We weren’t lucky to have fewer injuries than everyone else — our strength-and-conditioning coach is the best in America! And again: We’re just tougher!
As loath as we may be to admit it, a large percentage of a given college football season — with its small overall sample of games — is determined by the bounce of a pointy ball, the bend of a ligament and the whims of fate. Certain teams will end up with an unsustainably good turnover margin that turns on them the next year. Certain teams (often the same ones) will enjoy a great run of close-game fortune based on some combination of great coaching, sturdy quarterback play, timely special teams contributions … and massive amounts of unsustainable randomness. Certain teams will keep their starting lineups mostly intact for 12 or more games while another is watching its depth chart change dramatically on a week-to-week basis.
As we prepare for the 2025 college football season, it’s worth stepping back and looking at who did, and didn’t, get the bounces in 2024. Just because Lady Luck was (or wasn’t) on your side one year, doesn’t automatically mean your fortunes will flip the next, but that’s often how these things go. Be it turnovers, close-game fortune or injuries, let’s talk about the teams that were dealt the best and worst hands last fall.
Jump to a section:
Turnover luck | Close games luck
Injuries and general shuffling | Turnaround candidates
Turnover luck
In last year’s ACC championship game, Clemson bolted to a 24-7 halftime lead, then white-knuckled it to the finish. SMU came back to tie the score at 31 with only 16 seconds left, but Nolan Hauser‘s 56-yard field goal at the buzzer gave the Tigers a 34-31 victory and a spot in 2024’s College Football Playoff at Alabama’s expense.
In the first series of the game, Clemson’s T.J. Parker pulled a perfect sack-and-strip of SMU QB Kevin Jennings, forcing and falling on a loose ball at the SMU 33-yard line. Clemson scored two plays later to take a 7-0 lead. Late in the first quarter, Khalil Barnes picked off a Jennings pass near midfield, ending what could have become a scoring threat with one more first down. A few minutes later, Clemson’s Cade Klubnik fumbled at the end of a 14-yard gain, but tight end Jake Briningstool recovered it at midfield, preventing another potential scoring threat from developing. (Klubnik fumbled seven times in the 2024 season but lost only one of them.)
Early in the third quarter, after SMU cut Clemson’s lead to 24-14, David Eziomume fumbled the ensuing kickoff at the Clemson 6, but teammate Keith Adams Jr. recovered it right before two SMU players pounced.
Over 60 minutes, both teams fumbled twice, and Clemson defended (intercepted or broke up) eight passes to SMU’s seven. On average, 50% of fumbles are lost and about 21% of passes defended become INTs, so Clemson’s expected turnover margin in this game was plus-0.2 (because of the extra pass defended). The Tigers’ actual turnover margin was plus-2, a difference of 1.8 turnovers in a game they barely won.
Clemson was obviously a solid team in 2024, but the Tigers probably wouldn’t have reached the CFP without turnovers luck. For the season, they fumbled 16 times but lost only three, and comparing their expected (based on the averages above) and actual turnover margins, almost no one benefited more from the randomness of a bouncing ball.
It probably isn’t a surprise to see that, of last year’s 12 playoff teams, eight benefited from positive turnovers luck, and six were at plus-3.3 or higher. You’ve got to be lucky and good to win, right?
You aren’t often lucky for two straight years, though. It might be noteworthy to point out that, of the teams in Mark Schlabach’s Way-Too-Early 2025 rankings, five were in the top 20 in terms of turnovers luck: No. 5 Georgia, No. 7 Clemson, No. 9 BYU, No. 11 Iowa State and No. 17 Indiana (plus two others from his Teams Also Considered list: Army and Baylor).
It’s also noteworthy to point out that three teams on Schlabach’s list — No. 6 Oregon, No. 8 LSU and No. 15 SMU — ranked in the triple-digits in terms of turnovers luck. Oregon started the season 13-0 without the benefit of bounces. For that matter, Auburn, a team on the Also Considered list, ranked 125th in turnovers luck in a season that saw the Tigers go just 1-3 in one-score finishes. There might not have been a more what-could-have-been team in the country than Hugh Freeze’s Tigers.
Close games
One of my favorite tools in my statistical toolbox is what I call postgame win expectancy. The idea is to take all of a game’s key, predictive stats — all the things that end up feeding into my SP+ rankings — and basically toss them into the air and say, “With these stats, you could have expected to win this game X% of the time.”
Alabama‘s 40-35 loss to Vanderbilt on Oct. 5 was one of the most impactful results of the CFP race. It was also one of the least likely results of the season in terms of postgame win expectancy. Bama averaged 8.8 yards per play to Vandy’s 5.6, generated a 56% success rate* to Vandy’s 43% and scored touchdowns on all four of its trips into the red zone. It’s really hard to lose when you do all of that — in fact, the Crimson Tide’s postgame win expectancy was a whopping 98.5%. (You can see all postgame win expectancy data here)
(* Success rate: how frequently an offense is gaining at least 50% of necessary yardage on first down, 70% on second and 100% on third and fourth. It is one of the more reliable and predictive stats you’ll find, and it’s a big part of SP+.)
Vandy managed to overcome these stats in part because of two of the most perfect bounces you’ll ever see. In the first, Jalen Milroe had a pass batted at the line, and it deflected high into the air and, eventually, into the arms of Randon Fontenette, who caught it on the run and raced 29 yards for a touchdown and an early 13-0 lead.
In the second half, with Bama driving to potentially take the lead, Miles Capers sacked Milroe and forced a fumble; the ball sat on the ground for what felt like an eternity before Yilanan Ouattara outwrestled a Bama lineman for it. Instead of trailing, Vandy took over near midfield and scored seven plays later. It took turnovers luck and unlikely key-play execution — despite a 43% success rate, Diego Pavia and the Commodores went 12-for-18 on third down and 1-for-1 on fourth — for Vandy to turn a 1.5% postgame win expectancy into a victory. It also wasn’t Alabama’s only incredibly unlikely loss: The Tide were at 87.8% to beat Michigan in the ReliaQuest Bowl but fell 19-13.
(Ole Miss can feel the Tide’s pain: The Rebels were at 76.0% postgame win expectancy against Kentucky and 73.7% against Florida. There was only a 6% chance that they would lose both games, and even going 1-1 would have likely landed them a CFP bid. They lost both.)
Adding up each game’s postgame win expectancy is a nice way of seeing how many games a team should have won on average. I call this a team’s second-order win total. Alabama was at 10.7 second-order wins but went 9-4. That was one of the biggest differences of the season. Somehow, however, Iron Bowl rival Auburn was even more unfortunate.
Based solely on stats, Arkansas State should have won about four games, and Auburn should have won about eight. Instead, the Red Wolves went 8-5 and the Tigers went 5-7.
Comparing win totals to these second-order wins is one of the surest ways of identifying potential turnaround stories for the following season. In 2023, 15 teams had second-order win totals at least one game higher than their actual win totals — meaning they suffered from poor close-game fortune. Ten of those 15 teams saw their win totals increase by at least two games in 2024, including East Carolina (from 2-10 to 8-5), TCU (5-7 to 9-4), Pitt (3-9 to 7-6), Boise State (8-6 to 12-2) and Louisiana (6-7 to 10-4). On average, these 15 teams improved by 1.9 wins.
On the flip side, 19 teams overachieved their second-order win totals by at least 1.0 wins in 2023. This list includes both of 2023’s national title game participants, Washington and Michigan. The Huskies and Wolverines sank from a combined 29-1 in 2023 to 14-12 in 2024, and it could have been even worse. Michigan overachieved again, going 8-5 despite a second-order win total of 6.0. Other 2023 overachievers weren’t so lucky. Oklahoma State (from 10-4 to 3-9), Wyoming (from 9-4 to 3-9), Northwestern (from 8-5 to 4-8) and NC State (from 9-4 to 6-7) all won more games than the stats expected in 2023, and all of them crumpled to some degree in 2024. On average, the 19 overachieving teams regressed by 1.9 wins last fall.
It’s worth keeping in mind that several teams in Schlabach’s Way-Too-Early Top 25 — including No. 6 Oregon, No. 8 LSU, No. 11 Iowa State, No. 13 Illinois and, yes, No. 21 Michigan — all exceeded statistical expectations in wins last season, as did Also Considered teams like Army, Duke, Missouri and Texas Tech. The fact that Oregon and LSU overachieved while suffering from poor turnovers luck is (admittedly) rather unlikely and paints a conflicting picture.
Meanwhile, one should note that three Way-Too-Early teams — No. 12 Alabama, No. 23 Miami and No. 25 Ole Miss (plus Washington and, of course, Auburn from the Also Considered list) — all lost more games than expected last season. With just a little bit of good fortune, they could prove to be awfully underrated.
Injuries and general shuffling
Injuries are hard to define in college football — coaches are frequently canny in the information they do and do not provide, and with so many teams in FBS, it’s impossible to derive accurate data regarding how many games were missed due to injury.
We can glean quite a bit from starting lineups, however. Teams with lineups that barely changed throughout the season were probably pretty happy with their overall results, while teams with ever-changing lineups likely succumbed to lots of losses. Below, I’ve ranked teams using a simple ratio: I compared (a) the number of players who either started every game or started all but one for a given team to (b) the number of players who started only one or two games, likely as a stopgap. If you had far more of the former, your team likely avoided major injury issues and, with a couple of major exceptions, thrived. If you had more of the latter, the negative effects were probably pretty obvious.
Despite the presence of 1-11 Purdue and 2-10 Kennesaw State near the top of the list — Purdue fielded one of the worst power conference teams in recent memory and barely could blame injury for its issues — you can still see a decent correlation between a positive ratio and positive results. The six teams with a ratio of at least 2.8 or above went a combined 62-22 in 2024, while the teams with a 0.5 ratio or worse went 31-56.
Seven of nine conference champions had a ratio of at least 1.3, and 11 of the 12 CFP teams were at 1.44 or higher (five were at 2.6 or higher). Indiana, the most shocking of CFP teams, was second on the list above; epic disappointments like Oklahoma and, especially, Florida State were near the bottom. (The fact that Georgia won the SEC and reached the CFP despite a pretty terrible injury ratio speaks volumes about the depth Kirby Smart has built in Athens. Of course, the Dawgs also enjoyed solid turnovers luck.)
Major turnaround candidates
It’s fair to use this information as a reason for skepticism about teams like Indiana (turnovers luck and injuries luck), Clemson (turnovers luck), Iowa State (close-games luck), Penn State (injuries luck) or Sam Houston (all of the above, plus a coaching change), but let’s end on an optimistic note instead. Here are five teams that could pretty easily enjoy a big turnaround if Lady Luck is a little kinder.
Auburn Tigers: Auburn enjoyed a better success rate than its opponents (44.7% to 38.5%) and made more big plays as well (8.9% of plays gained 20-plus yards versus 5.7% for opponents). That makes it awfully hard to lose! But the Tigers made exactly the mistake they couldn’t make and managed to lose games with 94%, 76% and 61% postgame win expectancy. There’s nothing saying this was all bad luck, but even with a modest turnaround in fortune, the Tigers will have a very high ceiling in 2025.
Florida Gators: The Gators improved from 41st to 20th in SP+ and from 5-7 to 8-5 overall despite starting three quarterbacks and 12 different DBs and ranking 132nd on the list above. That says pretty spectacular things about their overall upside, especially considering their improved experience levels on the O-line, in the secondary and the general optimism about sophomore quarterback DJ Lagway.
Florida Atlantic Owls: Only one team ranked 111th or worse in all three of the tables above — turnovers luck (111th), second-order win difference (121st) and injury ratio (131st). You could use this information to make the case that the Owls shouldn’t have fired head coach Tom Herman, or you could simply say that new head coach Zach Kittley is pretty well-positioned to get some bounces and hit the ground running.
Florida State Seminoles: There was evidently plenty of poor fortune to go around in the Sunshine State last season, and while Mike Norvell’s Seminoles suffered an epic hangover on the field, they also didn’t get a single bounce: They were 129th in turnovers luck, 99th in second-order win difference and 110th in injury ratio. Norvell has brought in new coordinators and plenty of new players, and the Noles are almost guaranteed to jump up from 2-10. With a little luck, that jump could be a pretty big one.
Utah Utes: Along with UCF, Utah was one of only two teams to start four different quarterbacks in 2024. The Utes were also among only four teams to start at least 11 different receivers or tight ends and among five teams to start at least nine defensive linemen. If you’re looking for an easy explanation for how they fell from 65th to 96th in offensive SP+ and from 8-5 to 5-7 overall, that’s pretty succinct and telling.
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