It is time to take a dive into the remains of the 2019 basketball
World Championship, now dubbed simply as the FIBA Cup.
The Serbian and the American national teams failed miserably - what was supposed to be a quite interesting finals became a torture and a missed opportunity for both teams.
While the US team couldn't count on a dozen All-Star players in their prime, it is too easy to dismiss the talent that coach Pop has at his disposal. The US has such an overwhelmingly huge player base and they have been playing under the FIBA / international rules for so long, that I find it difficult to dismiss their teams lack of success and file it under the missing key players file.
Serbia, on the other hand, is maybe an even bigger dissapointment. A lineup featuring Nikola Jokic, a top 5 NBA player and arguably the most dominant center in the post-Shaq era, an NBA sharpshooter (Bogdanovic) just entering his prime, Milutinov - probably the best center in Europe... It just wasn't enough for two expirienced teams (the amazing Argentina and an even more amazing Spain), filled with veterans playing well beyond their primes.
This is not an attempt to drill down and find the quantitative causes of the Underdogs Cup. FIBA presents us with a very oldschool set of basketball data: basic individual and team stats, and a couple of extras like efficiency and plus/minus. I tried to visualize some of the data provided on the official World Cup website, and I believe that even this data tells a story, at least a small part of the story.
Team stats are maybe more interesting, but here again we can see how the Cup system skews the data: an excellent example is the Serbian team - their group blowout victories inflated the statsheets beyond recognition... In fact, if you were to give this statsheet or visualization to somebody who knows nothing about the teams, would he be able to tell you who won it all? Or who played in the finals? I don't think so.
A remedy would be a partial analysis, exploring the statistics of the second round, but by then, the damage was already done...
...or an awesome visual tool? I do not know, you'll have to decide for yourself. I was planning on making a couple of scatterplots in order to visaulize some relationships. This basketball dataset is pretty difficult to represent in a scatterplot, since the data is higly interdependent and correlated as all of the basic stats are a function of the same variable: did the player perform well or not.
The World Cup isn't a league, it is not even a championship, that is why it is called a cup. The system might be improved and yes, maybe some teams are a bit amateurish, but hey! the purpose of the Cup is, among others, the growth of basketball in precisely thos countries - the ones we, the cool basketball countries, regularly beat with 20 to 40 points.