By Eurohoops team/ info@eurohoops.net
With almost two-thirds of the regular season on the books, there’s a pretty clear image about every team’s abilities and goals.
The playoffs race includes a record number of 13 teams with chances, however, the general consensus at this point is that two spots are still up for grabs — the seventh and eighth positions.
Can all those assumptions be measured and produce a result that can be backed up by numbers?
That was our question to the good people at Statathlon, and this is the result of their simulation. Statathlon provided not only a measure of the degree of difficulty of each team’s remaining schedule but also a prediction for the final standings of the regular season.
And if you are fans of KIROLBET Baskonia and FC Bayern Munich, you should hope that this simulation will be proved correct.
Introduction
Giving an estimate for the total number of wins of a team is one of the biggest challenges faced by statisticians and analysts involved in basketball. Those results can be affected by different factors that in many cases can be unpredictable or difficult to recognize and take into consideration.
Methodology
To predict the total number of wins of all 16 Euroleague Basketball for the remainder of the regular season, and therefore the final standings, an index developed by Statathlon was used. It is a modification of two indexes applied to the NBA and the NCAA, ESPN’s Basketball Power Index and Strength of Schedule, and is expressed by a linear function. Factors being used are:
Strength and form of an opponent: At the beginning of the season, teams were split into four groups according to their strength and expectations.
Group 1 (Buducnost VOLI Podgorica, Herbalife Gran Canaria, Darussafaka Tekfen Istanbul, Bayern)
Group 2 (Maccabi FOX Tel Aviv, AX Armani Olimpia Milan, Khimki Moscow Region and Zalgiris Kaunas)
Group 3 (Baskonia, Anadolu Efes Istanbul, FC Barcelona Lassa, Panathinaikos OPAP Athens)
Group 4 (Olympiacos Piraeus, CSKA Moscow, Fenerbahce Istanbul, Real Madrid)
The groups were re-evaluated every six games, as some teams improved (e.g. Bayern) or worsened (e.g. Khimki). Facing a stronger opponent decreases the winning chances of a team.
- Offensive and Defensive Efficiency: Counted as total points for minus total points against (per 70 possessions).
- Home-court advantage: Teams playing at home are more likely to win games than they are playing away.
- Travel distance and fatigue: Long-distance trips add fatigue to teams, as do back-to-back games (defined as less than two rest days). It also adjusts for the strength and form of the first opponent in back-to-back games.
- Key player absence: The index also takes into consideration the absences of key players. For example, Alexey Shved of Khimki has missed 11 games so far this season and Tornike Shengelia the last four games of Baskonia.
Different weight is assigned to each factor to calculate a team score for each game. A team with a score of 10 means it is more likely to win a game when its opponent has a score greater than that. Using this algorithm, Statathlon simulated the first 19 rounds of the EuroLeague Regular Season, where it predicted correctly approximately 73% of results.
To simulate the rest of the season, two assumptions were made: 1) There will be no key player absences, and 2) Teams will maintain their current strength and form, as of Round 19.