Ever since I started following sports as a little kid, I’ve been very intrigued by numbers. I collected baseball and basketball cards, and it was from this hobby that my interest in statistics took off. Knowing the number of points, rebounds or assists that a player put up gave me the feeling of being closer to a player, or at least understanding where he excelled. I knew, for instance, that an NBA player who averaged 15 points per game was pretty good, that most stars averaged 25 or more, and an elite few socred around 30 a night. The stats also came in handy because I lived in a city that didn’t have an NBA team, and still doesn’t to this day. For most of the season, my only pro basketball intake came in the form of highlights and agate data. Once the playoffs bled into the summer, and I could stay up later, I could finally sit down and watch all these guys from the glossy cardboard rectangles in the albums I kept in my room. While I enjoyed watching college basketball more, and still do, NCAA regulations limited the production of printed cards with college players’ likenesses, so I didn’t have the same kind of information available to me. It also didn’t help that the Internet hadn’t evolved to the point where access to stats was readily available.
The Internet eventually caught up and my craving was satisfied. A few years ago, though, somewhat on the heels of the statistical revolution in baseball, experts started turning up new data. Many people much smarter than me have cranked out mountains of research towards this objective, but the theme has changed from measuring performance per game to measuring performance per possession, accounting for the different paces at which different teams play. This phrase that characterizes this new realm of analysis is often referred to as “tempo-free.” To get up to speed, you can check out the blog’s glossary.
Tempo-free stats aren’t such a new development, though. Dean Smith, the longtime coach at North Carolina, routinely charted individual possessions, as did many other coaches. What is new is that increasing amount of data has been made available to us fans at places like StatSheet and Ken Pomeroy’s site. Over the last two seasons, I’ve immersed myself in some of this new data in an effort to understand what habits are prevalent among successful teams, and how teams can win despite performances that would normally cause them to lose. Let me show you what I mean.
The Orange’s win over UConn in Hartford last season was notable for a few reasons, all of which ironically don’t have much to do with stats at all:
- It was a win that SU needed badly after dropping four straight games.
- Syracuse hadn’t won on either of the Huskies’ home courts since 1999.
- This was the first Syracuse game following the ridiculous point-shaving rumors.
- It was Dion Waiters’ first game back after a stay in Jim Boeheim’s doghouse.
The game itself wasn’t prettier than any of those storylines, but the Orange escaped with a messy 66-58 victory. What was most interesting to me was that Syracuse won the game despite shooting a paltry 37.7% from the floor. That kind of a shooting percentage is normally liable to get you blown out of the gym. While it helped that Syracuse played tight defense in holding UConn to a similarly low shooting percentage, SU saved itself by being very effective in two other components of the game. The Orange turned the ball over just 13% of the time on offense, compared with its usual turnover rate of 18%. To provide some context, Wisconsin led the nation in turnover rate over the course of last season with a rate of 13.4%. Simply by holding onto the ball and not giving the Huskies extra chances, the Orange was able to offset its shooting woes.
The other area was in offensive rebounding rate. In that game, SU reeled in a very high percentage of their own misses, 42.5% percent of them, to be precise. The ability to to follow shots led to Syracuse scoring 19 second chance points, or roughly 25% of their scoring total from that game. Baye Moussa Keita was especially huge with 11 rebounds and three on the offensive glass.
There’s a perceived disconnect between the scouting types who prefer to evaluate based on what they watch and the stat-based community. While I lean toward the second of those groups, I think they can coexist to give us more comprehensive insight into how the game works. If you’ve ever tuned into an SEC game, you’ve heard Jimmy Dykes mention his “eye test,” a concept that I hate, but tempo-free stats have slowly bled into the telecasts and as long as the game will be played, there will be matchups whose outcomes fall outside the norm of traditional projections.