Archive for the ‘Euroleague’ Category
Mega Vizura and Partizan field the youngest rosters of active players in top level European basketball. The ABA league, traditionally a hotbed for basketball prospects, maintains its status as the youngest league.
In weighted roster age, which accounts for individual minutes played and therefore fully accounts for players that play large minutes and ignores those who don’t play at all, the ABA League heads the list of my arbitrary list of top ten domestic European leagues, at 26.0 years.
Mega Vizura (3-4), Partizan (6-1) and CajaSol (2-3) are the somewhat expected top three youngest rosters on the continent, followed by the lesser known Leuven Bears with early season sensation Tu Holloway, who stand at a respectable 4-3 in the Ethias League.
You see, this has not been just a Cinderella story. This was a first glimpse at a rising power. Partizan’s method allowed them to make the playoffs two seasons in a row back in 2008 and 2009, before selling the rights to their core players (Nole Veličković, Uroš Tripković, Nikola Peković, Milenko Tepić) and advancing to the final four in 2010. If financial considerations do not tear this team apart, the rest of Europe should consider themselves warned.
That was what the future looked like (from where I was sitting at least) for Partizan 11 months ago. Having been knocked out of the top-16 in heartbreaking fashion, the Belgrade club had a lot to look forward to. And after surviving another summer of financial uncertainty, they entered this season ready to shock a world that somehow expected to be shocked. Vladimir Lucic, an energetic forward who had come to personify Partizan’s fiery playing style was dealt to Valencia. But the front office managed to keep the core of the team together and reinforce it with a couple of exciting French prospects. Then the season tipped off and we experienced a letdown.
Back in 1985 Amos Tversky, an Israeli behavioural psychologist, published one of the earliest papers that could be categorised as ‘Advanced Analytics’, where he proved that shots should be categorised as individual events. This went against the assumed knowledge that players and teams should ride the ‘hot hand’ and that if a player had made a few shots in a row, he was more likely to make his next shot. Though widely accepted now at the time Tversky’s theory was rubbished with legendary Celtics coach and GM Red Auerbach commenting “Who is this guy? So he makes a study. I couldn’t care less” and Bob Knight saying “There are so many variables involved in shooting the basketball that a paper like this really doesn’t mean anything.” This led Tversky to assert “I’ve been in a thousand arguments over this topic. I’ve won them all and I’ve convinced no one.”
While writing the article published yesterday on shot allocation I thought a lot about just how teams can achieve an efficient shot allocation, having read long-time Tversky collaborator Daniel Kahnemann’s book “Thinking, Fast and Slow” (which is definitely well worth a read) shortly before I begun thinking about the “hot hand” fallacy. The issue at hand with the “hot hand” fallacy is that humans are intuitively bad statisticians, and we will instinctively ascribe some kind of causality when we see something that we believe is a statistical anomaly (like a player hitting three or four shots in a row), which is actually within a random distribution. As with this being an issue with instinct, shot allocations are too guided by instinct, players will take the shots that they believe are best for the team at the spur of the moment, it is the job of coaches to guide the instinct of players. But, given that the shot allocations are imperfect, how can we find a short cut to efficiency?
We all have our own gripes while watching basketball, mainly about players taking bad shots (my own personal gripes on this topic are typically aimed at Pero Antic), but how can we qualify whether a player is taking a poor shot in the context of his team? Furthermore, how can we quantify whether an entire team is being effective in its allocation of shots between the available players?
As a former Economics student when the word ‘allocate’ appears my mind automatically makes a jump to allocative efficiency, which is a condition that leads to maximisation of economic welfare in a market, as the value put on a good or service by the consumer is equal to the cost of production, that is to put it in economical terms, where Marginal Benefit is equal to Marginal Cost. To put this in laymans terms it means that any dead weight is lost and that any resources are allocated to their best use.
For a basketball event so important as the turnover, we know too little about it. Here is an attempt to change that.
In his 2004 book “Basketball on Paper”, a standard reference for basketball statheads, Dean Oliver outlined four key factors in basketball. We know them as “The Four Factors”:
- Shooting percentage (40%)
- Turnovers (20%)
- Rebounds (15%)
- Earning and making free throws (10%)
Here’s a review of some of the information we have on the four separate factors.
There is plenty of shooting data on the net. Eli Witus, now vice president of Houston Rockets basketball operations, produced the first visuals in 2008, Hoopdata followed in non-visual form. Basketball-Reference has them and so does, at last, NBA.com. Several European leagues have shot charts in their live stats applications, and when summarized, they look like this. Kirk Goldsberry, a geography scholar at Harvard, took spatial analysis to another level over the last couple of years, combining the gold mine that is optical tracking data with his geo-mapping knowledge and tools.