Category Archives: Statgeekery
With Derrick Rose‘s 2011 MVP looking like a foregone conclusion, it seems only natural to compare his campaign to that of Allen Iverson in 2001, the year another popular guard won the MVP despite not being the game’s most talented player.
Here’s the numerical tale of the tape for A.I. and D-Rose, with Rose extrapolated to 82 team games: (Glossary)
Statistically, the two players are incredibly comparable. If you translate Iverson from the 103.0 league-ORtg environment of 2001 to the league ORtg of 107.1 in 2011, his ORtg/%Poss/DRtg becomes 110.5/33.8/103.0, production that is basically equivalent to Rose’s after adjusting for usage.
With the 2010-11 NCAA basketball season technically commencing this week, let’s return to these rankings…
15. Connecticut Huskies (+14.16 SRS)
Record: 682-312 (.686)
Prominent Coaches: Jim Calhoun
Best NCAA Finish: Won NCAA Championship (1999, 2004)
Two national titles in the last 12 years makes up for a mediocre first half of the 1980s under Dom Perno, as the leadership of Calhoun has transformed Storrs into an unlikely national hoops hotbed. And to think that it all started with Scott Burrell & Tate George…
Last Friday, I posted about teams that formed as potent a slashing combo as the new LeBron James–Dwyane Wade duo in Miami, and found that in an incredibly small sample of similar cases (3, to be exact), at least one — if not both — of the players had to change their playing style to accommodate their new circumstance. A lot of people asked about the general effect of the new team member on the offense, though, so today I wanted to quickly follow up and look at whether the driving tendency of the added player correlated to the amount of offensive improvement the team saw.
One common observation about the new-look Miami Heat goes something like this:
- Dwyane Wade is a great perimeter player who makes his living attacking the basket. He’s unstoppable when he drives into the lane, but not as good when you force him to shoot a jump shot.
- LeBron James is also a great perimeter player who makes his living attacking the basket. He, too, is unstoppable when he drives into the lane, but not as good when you force him to shoot a jump shot.
- Won’t this redundancy in skills make the Heat easier to defend?
If only we could quantify this dilemma, find similar situations in the past where two hard-driving teammates joined forces, and see if their offenses were as potent as expected…
Oh, wait, we can.
Enter good old Free Throw Rate (FTA/FGA). Because the majority of fouls are assessed on interior shooting attempts and/or aggressive offensive plays, FTR is actually a pretty good indicator of where a player likes to operate from on offense. Players like Glen Rice and Dennis Scott were known for their low FTRs because they took a ton of perimeter jumpers, shots on which a foul would land you in the serious doghouse. And at the other end of the spectrum there’s Reggie Evans, whose legendary FTRs tell the story of a player who rarely attempts a shot outside of point-blank range. Obviously there are some players who are exceptions to this rule, but the majority of players’ inside-outside tendencies can be described simply by looking at FTA/FGA.
So that should be the starting point in examining the issue of hard-driving teammates. The next step is to compare everyone’s FTR to some universal standard, and to do that I borrowed this method from PFR’s Doug Drinen. I don’t want to bore you with the details, but it basically compares everyone to the league average; 100 is average, numbers greater than 100 mean the player attacks the rim more than the average player, and numbers under 100 mean the player is less aggressive than the average player. The theory is that if we just look at these “FTR Index” numbers for perimeter players (PG, SG, SF), we can find players who drove to the basket the most, which best describes LeBron and D-Wade’s playing style.
Several times in the past, I’ve looked at what I called “Team Continuity” — that is, the amount of minutes/possessions/etc. that a team gave to players who had been on their roster the year before. Today, I want to extend the concept to the NBA as a whole and examine league continuity, specifically the 5-year periods since the merger in which the league had the biggest influx of new talent.
Following up on yesterday’s post about newly-formed “Big Twos”, here are notable “Big Threes” from throughout NBA history, formed by taking at least 1 established star from another team. Just to be clear, this is not a list of the Greatest Big Threes Ever; rather, this is a list of combinations featuring players who had been the biggest focal points of their teams the previous year, and then were put together on one team, with each having to adjust to not being the clear-cut alpha dog anymore. Let’s go to the list: (note that none of these would even come close to matching the 97.3% combined possession rate the proposed LeBron James–Dwyane Wade–Chris Bosh trio had in 2009-10)
1. Michael Jordan, Jerry Stackhouse, & Larry Hughes, 2003 Wizards
Previous Combined %Poss: 89.6% (Jordan – 34.6%; Stackhouse – 32.1%; Hughes – 22.9%)
Previous Team Offensive Rating: 104.8
New Team Offensive Rating: 103.0
New Split of Possessions: 27% (Jordan) – 27% (Stackhouse) – 21% (Hughes)
Comparability to James-Wade-Bosh: Low. We touched on this one yesterday, but it’s tough to remember that Larry Hughes was also added to that Wizards team after being the primary facilitator on a Warriors squad that won just 21 games in 2002. The raw talent was certainly there for this group, but they were in the wrong place at the wrong time — Jordan had peaked 5-10 years earlier, Hughes wouldn’t peak until 2 years later, and Stack was what he always was, a high-volume/low-efficiency gunner.
There’s been a lot of talk about how Kobe Bryant’s legacy is “on the line” tonight. Win, and he could become the Greatest Laker Ever™ (Bryant would have 5 championships in Forum Blue & Gold, tying him with Magic Johnson, Kareem Abdul-Jabbar and George Mikan as the franchise’s winningest winner); lose, and it would be his 3rd Finals loss (clearly a blemish from which his reputation could never recover). In short, Bryant supposedly won’t be the same caliber basketball player tomorrow morning if the Lakers don’t win tonight.
In case you can’t tell, I think that’s an extremely flawed and childish way to look at the question of who the greatest Laker was/is. Kobe winning a ring tonight adds to his resume in some ways, but it’s not like getting to 5 titles automatically ties his career with Magic Johnson’s, nor is it true that he could never surpass Magic if L.A. loses tonight. Winning a ring is the ultimate team accomplishment, but we have much better ways to parse out player contributions than to lazily took at championship totals and blindly base our evaluations on them alone.
In Part II of this series, I developed a method of estimating a team’s probability of winning the NBA Championship based on the allocation of their possessions among their top 5 players. The idea is that, assuming 2 teams are championship-caliber, the one who follows the time-tested pattern of Star 1a + Star 1b + 3 role players will be more likely to win a championship. Today, I’m going to apply this to all regular-season teams in NBA history, and see which teams were theoretically built for postseason success, then look at what actually happened to them.
First, we need to define what it means to be a “championship-caliber team”. Historically, the average regular-season SRS of all NBA champions is 6.07, and the median SRS is 6.059. Obviously, teams have won with SRS scores of under 6 (the 2006 Heat were the last team to do so), but as a general rule, if you post an SRS of 6 or greater during the regular-season, you have established yourself as either the odds-on favorite or at least one of the leading candidates to win the NBA title, which is what we’re going for here.
After yesterday’s post about optimal championship usage patterns, I got a lot of good feedback about possible alternative versions of the same study that would better capture the effect I was going for. When setting up for the initial study, I struggled between sorting by minutes played and by raw modified shot attempts (MSA), each of which had unique advantages. But a nice compromise (suggested by reader Brian) would be to isolate the top 5 players on each team by minutes — thereby approximating their most frequent 5-man unit — and then sort by MSA%, the percentage of team MSA that each player took while on the floor:
In basketball perhaps more than any other sport, the concept of team-building — creating a cohesive group that fits together and may be greater than the sum of its parts — is phenomenally important. In baseball, a sport dominated by one-on-one matchups, not a whole lot of consideration has to be made for how teammates work together; to make a great team, you basically grab the 25 best players you can, throw them together, and watch them produce. But in basketball, teammates have to work together while simultaneously “competing” for touches & shots. Throw together a baseball lineup of 9 guys who each create 100 runs, you’ll probably score 900 runs; throw together a basketball lineup of 5 20 PPG scorers, you probably won’t score 100 PPG. There’s no upper limit on the number of runs the baseball lineup can produce, but there is an upper limit to the points the basketball lineup scores, because teams are limited by a finite number of minutes in a game, and as a result, lineups are limited by a finite number of touches & shots to be allocated to the individual players.
That’s why a stat like Possession% (the % of team possessions a player uses while on the floor) is important in looking at how the pieces of a team fit together. A lineup of All-Stars would be interesting, but perhaps a less-talented lineup with one 26% usage guy, two 20% guys, an 18% guy, and a 16% guy would be even better if the All-Stars are not happy with the way they fit together or are unable to operate at peak efficiency in lesser roles, while the less talented lineup features players who are all at their optimal usage levels. The whole of the latter would be greater than the sum of the former’s parts.