Deep Dives: Measuring Level of Competition Around the World

Jun 25, 2015; Brooklyn, NY, USA; Kristaps Porzingis (SPN) reacts after being selected as the number four overall pick to the Miami Heat in the first round of the 2015 NBA Draft at Barclays Center. Mandatory Credit: Brad Penner-USA TODAY Sports
Jun 25, 2015; Brooklyn, NY, USA; Kristaps Porzingis (SPN) reacts after being selected as the number four overall pick to the Miami Heat in the first round of the 2015 NBA Draft at Barclays Center. Mandatory Credit: Brad Penner-USA TODAY Sports /
Jun 25, 2015; Brooklyn, NY, USA; Kristaps Porzingis (SPN) reacts after being selected as the number four overall pick to the Miami Heat in the first round of the 2015 NBA Draft at Barclays Center. Mandatory Credit: Brad Penner-USA TODAY Sports
Jun 25, 2015; Brooklyn, NY, USA; Kristaps Porzingis (SPN) reacts after being selected as the number four overall pick to the Miami Heat in the first round of the 2015 NBA Draft at Barclays Center. Mandatory Credit: Brad Penner-USA TODAY Sports /

I am interested in projecting players into the NBA. I have done a lot of work projecting players from college, and in the past couple of years expanded my projections to some of the bigger international professional leagues. I am currently finalizing my efforts to include every league for which I can find publicly available data. I will make these projections available here at Nylon Calculus soon, but in the meantime I want to share one piece of this puzzle that serves as an interesting stand-alone resource.

One key part of projecting across different leagues is quantifying the relative strength of those leagues, or more specifically, identifying how heavily to weight performances in different venues in order to arrive at the most accurate player projections. Even if you do not ultimately put much stock in my projections, knowing how the many different competitions around the world stack up might make it easier to balance your subjective assessments of player performances.

The Method:

[Warning… the following gets a bit hairy. If you just want to see the rankings and commentary, skip past this section. If you want to be able to understand how I got there, read on.]

The process starts with a regression model that uses a collection of variables including age, per-possession boxscore statistics, and height to project NBA performance. The goal of this type of model is to find the weights for these variables that do the best job of accurately recovering the observed value of players in the NBA based on their pre-NBA production. Unfortunately, because this regression includes player-seasons from competitive venues as dramatically different as the Olympics and domestic European B leagues, it is going to lead to some whacky results. Not only will the resulting model under or overrate players depending on their competitive venue, but it will have a tough time understanding the true relationship between statistical production and ultimate NBA performance because it is clouded by strength-of-schedule effects. In order to get around this problem, I include a term that finds the optimal value to debit players in each competition in order to arrive at accurate predictions. As a result, I get “fixed effects” that give a reliable weighting of the importance of each variable, and “random effects” that take a stab at explaining the difference between all of the possible competitive venues.

However, this does not get us far enough. Not only is the sample of players who competed in the NBA and many of the international leagues extremely small, but there are even more leagues I am interested in projecting that have never produced an NBA player. To build more trustworthy competition rankings, and include the entire set of leagues, I need to take an additional step. At this stage, I project every player-season in the data into the NBA using the fixed effects I found above. This gives competition-agnostic projections for a player based on his production in a given venue. To this point, players in the Irish Superleague are held to the same standard as those in the Spanish ACB. Obviously this needs to be corrected. I accomplish this by looking at how the projections for players in each competition differ on average to those same players’ scores in other venues. This results in a collection of relative scores that are ultimately anchored in the random effects I found above. For example, I may not know how a player from the Georgian Super Liga does in the NBA, but I know how he performs in Eurobasket and from the random effects above, I have an estimate for how Eurobasket performance translates to the NBA. The average increase or decrease in ‘value’ between the Georgian Super Liga and Eurobasket gives me one estimate of competition value by simply adding that difference to the original estimated value for Eurobasket.  I do the same to arrive at unique estimated values for the Georgian Super Liga relative to each of the other leagues that it shared players with. I then calculate a single estimated competition value using the mean across those estimates (weighted by number of cases). This gives a first approximation for the relative value of all the competitions not initially captured above. It also improves the estimates for the random effects by taking information from many additional between league tranformations. After this step it gets a little more complicated, because in improving the estimates, all of the values that were used to find relative values are themselves moving around.  In order to find a stable set of relative values, I need to iterate this process many times until the estimates settle into a final stable set of values. After all of this is finished, I translate the values into standardized scores such that the most average competitive venue in the data scores a 0, and a venue that is one standard-deviation more difficult than average scores a 1, while a venue one standard-deviation less difficult scores a -1.

Relative Competition Scores:

One caveat before we move on to the numbers.  These are not exactly rankings for strength of competition in different conferences.  Instead, these are a measure of what statistical production in each league says about NBA potential.  A player in a competition with a lower relative score needs to put up more impressive numbers to be considered on the same level as a player in a higher scoring competition.  Obviously this concept is highly correlated with “strength of competition”, but it is not exactly the same thing.

Here are the results:

1NBA Playoffs200220153.84
3Team USA Exhibitions201220143.03
4Olympic Games200420122.56
5EuroBasket Final Round200320052.21
6Diamond Ball Tournament 200420082.14
7FIBA World Cup200220141.85
8London Invitational Tournament201120111.78
11Tuto Marchand Continental Cup200720151.57
12Spanish ACB200220161.52
13FIBA Americas Championship200320151.50
14Team Canada Exhibitions201320131.49
15Pan American Games200720151.39
16Spanish Cup201220151.38
17Olympic Qualifying Tournament 200820121.37
18Euroleague Qualification201220151.22
19NBA D-League200220151.21
20Italian Cup201220151.17
21EuroBasket Semi-Final Round200320031.16
22Russian PBL201220131.13
23Italian Lega Basket Serie A200220161.12
24Intercontinental Cup201420151.10
25Borislav Stankovic Cup201320130.97
26PAC 10 NCAA200220110.96
27South American Championship 200320080.96
28Saporta Cup200220020.93
30VTB United League201220160.87
31French Leaders Cup LNB201320150.84
32Adriatic League Liga ABA200220160.82
33Australian NBL201220160.80
34CentroBasket 200320080.79
35Turkish BSL201220160.77
36ACC NCAA200220150.76
37French LNB Pro A200320160.69
38SEC NCAA200220150.68
39German BBL201220160.65
40Big East NCAA200220150.56
41Belgium Scooore League201220160.56
42PAC 12 NCAA201220150.54
43Korac Cup200220020.53
44Israeli BSL201220160.52
45Big 12 NCAA200220150.50
46Baltic League BBL Elite Division201220120.49
47EuroBasket Preliminary Round200320030.49
48Big 10200220150.48
49BBL Cup201320150.47
50EuroBasket Qualification201220140.47
52Eurochallenge Qualification201220130.46
53FIBA Africa Championship200620150.45
54Greek HEBA A1200220160.43
55EuroBasket Qualifying Round200320030.42
56Puerto Rican BSN201220150.41
57World Championships u21200520050.40
58Lithuanian LKL201220160.33
59EuroCup Challenge200320060.33
60Conference USA NCAA200220150.31
61Spanish LEB Gold201220160.30
62MWC NCAA200220150.30
63WAC USA200220150.28
64WCC USA200220150.25
65Atlantic 10 NCAA200220150.25
66Sun Belt NCAA200220150.23
67Argentinian Liga A201220160.23
68Polish TBL201220160.22
69AAC NCAA201420150.21
70Canadian NBL201220150.20
71Ukrainian Superleague201220150.19
72Cypriot Division A201220150.15
73Serbian KLS201220160.14
74FIBA Europe Regional Challenge Cup200320030.10
75CAA NCAA200220150.06
76Spanish LEB Silver201220160.06
77Venezuelan LPB201220150.04
78French LNB Pro B201220160.03
79Balkan BIBL201220160.00
80European Championship Final Round u20200220150.00
81Liga Americas20122015-0.02
82The Americas Tournament u2120042004-0.02
83MAC NCAA20022015-0.02
84Premier Basketball League20122012-0.03
85Italian Serie A2 Basket20122016-0.04
86Chinese CBA20122015-0.04
87Slovenian SKL20122016-0.04
88Mid-Continent NCAA20022007-0.04
89Horizon NCAA20022015-0.04
90Big Sky NCAA20022015-0.05
91MVC NCAA20022015-0.05
92Finnish Korisliiga20122016-0.05
93Baltic League BBL Challenge Cup20122012-0.06
94German Pro A20132016-0.08
95Ivy League NCAA20022015-0.10
96Baltic Basketball League20132016-0.10
97Big West NCAA20022015-0.11
98FIBA Asia Championship20072015-0.12
99Mexican LNBP20122016-0.13
100Romanian Divizia A20122016-0.14
101Southern NCAA20022015-0.14
102MEAC NCAA20022015-0.15
103MAAC NCAA20022015-0.15
104Austrian A Bundesliga20122016-0.16
105Croatian A 1 Liga20122016-0.16
106NEC NCAA20022015-0.17
107Latvian LBL20122016-0.18
108Southland NCAA20022015-0.19
109Brazilian NBB20122015-0.19
110Summit NCAA20082015-0.19
111Swiss LNA20122016-0.22
112World Championships u1920032015-0.23
113Atlantic Sun NCAA20022015-0.24
114AEC NCAA20022015-0.25
115Bulgarian NBL20122016-0.25
116Netherlands DBL20122016-0.29
117NIKE Global Challenge prep20072015-0.30
118Swedish Basketligan20122016-0.30
119Big South NCAA20022015-0.30
120FIBA Europe Cup20162016-0.31
121Universiade univ20112015-0.32
122Estonian KML20122016-0.32
123Independent NCAA20022015-0.39
124OVC NCAA20022015-0.42
125adidas Nations prep20122015-0.43
126Czech NBL20122016-0.44
127Macedonian Superleague20122016-0.46
128Turkish TBL20122016-0.46
129European Championship Qualifying Round u2020022004-0.48
130SWAC NCAA20022015-0.48
131European Championship Final Round u1820022015-0.49
132Montenegrin Prva A Liga20122016-0.49
133French NM120132016-0.50
134Americas Championships u1820062014-0.53
135Luxembourg Total League20132016-0.53
136GWC NCAA20102013-0.55
137Patriot League NCAA20022015-0.55
138Liga Sudamericana20122016-0.55
139Mexican CIBACOPA20122015-0.57
140Slovakian Extraliga20122016-0.59
141Hungarian NBIA20122016-0.59
142Portuguese LPB20122016-0.59
143British BBL20122016-0.62
144Ciutat De LHospitalet Tournament ANGT prep20122015-0.63
145Lebanese Division A20132015-0.64
146McDonalds All-American prep20022015-0.65
147adidas Next Generation Tournament20122015-0.65
148Bosnian BiH Liga20122016-0.65
149World Championships u1720102014-0.70
150International Basketball League20122014-0.76
151Jordan Classic prep20022015-0.79
152South Korean KBL20122016-0.81
153European Championship Challenge Round u1820022004-0.85
154Kazakhstani National League20132015-0.86
155Danish Basketligaen20122016-0.90
156Nike Hoop Summit prep20042015-0.93
157Georgian Super Liga20132016-0.93
158Albert Schweitzer Tournament u1820142014-0.96
159European Championship B u1820052015-0.97
160European Championship Final Round u1620032015-0.97
161European Championship B u2020052015-0.99
162German Pro B20152016-1.04
163New Zealand NBL20122015-1.04
164Filipino PBA20122016-1.04
165Japanese JBL20122013-1.07
166Citta Di Roma Tournament ANGT prep20122015-1.08
167Siauliu Tournament ANGT prep20132013-1.17
168Ukrainian SL Favorit Sport20162016-1.23
169National Basketball League of Japan20142016-1.25
170European Championships C u1820132015-1.31
171Belgrade Tournament ANGT prep20122015-1.31
172European Championship Qualifying Round u1620032003-1.34
173Icelandic Dominos League20132016-1.34
174Belarusian BPL20122016-1.37
175European Championship Qualifying Round u1820022004-1.38
176Kaunas Tournament ANGT prep20122015-1.39
177European Championship Challenge Round u1620032003-1.44
178Americas Championships u1620092015-1.56
179Norwegian BLNO20122016-1.64
180Irish Superleague20122016-1.78
181European Championship B u1620042015-1.79
182All-American Championship prep20112012-1.92
183Derby Classic prep20102015-2.04
184Jordan Classic Regional prep20082015-2.12
185Jordan Classic International prep20082015-2.24
186European Championships C u1620132015-2.78

So…. what are some fun things that jump out of this list?

The NBA and of course the NBA Playoffs are by far the most competitive basketball venues, while prep and cadet tournaments occupy the bottom slots.  These findings should not surprise anybody, but they at least help the method pass the smell test.

You might notice that I broke up the NCAA into different conferences.  This lets us not only see how college competition compares to international professional leagues, but also helps contextualize the difference between major and mid-major college conferences.  The range in relative difficulty for the NCAA ended up being larger than I expected.  The Patriot League and Great West conferences are at the bottom , just behind the Luxembourg Total League.  Meanwhile, the major conferences are on par with some of the better European domestic leagues.

Those familiar with international basketball might be surprised to see the two Russian leagues (Russian PBL and VTB United League) trailing only the Spanish ACB among domestic competitions.

The Chinese Basketball Association rates lower than I expected.  The CBA sees a lot of players with at least marginal NBA talent.  Many international scouts malign the defensive effort in the CBA, which would allow players to put up gaudy but empty numbers.  In addition, the talent skew resulting from low-level domestic players and a few high-level American players may result in extreme usage for those good players, who are ultimately the individuals most represented in between league comparisons.