Assist/Turnover ratio: Its impact on the success of a team

It is a general consensus that in basketball the best team usually wins. This has proven to be quite accurate for many decades. One question that raises though is, in which extent does the team stats have to do with the long – term success of a club. Is there any stats category that could tell us the truth about it?

For the purpose of this research the study case will be Euroleague Basketball, the most prestigious continental club competition in Europe. The starting season will be the 2000/2001, when the competition was introduced with its current format. There are also two notes to keep in mind:

  • Till 2002, 32 teams qualified. Then, from 2001 to 2016 24 teams were participating each year, and now this number has been reduced to 16.

  • Euroleague changed the way it counts a pass as an assist in 2012. Before that year, the passes that lead to a shooting foul (where the player scores at least 1 of the free – throws), were not considered as assists.

This research will be focused to one that belongs to the so – called “advanced stats” category : the assist to turnover ratio. This measurement is effective, as quite often the assists and the turnovers are linked both with the offensive and defensive performance of a team because :

  • More assists usually lead to more field goals scored.

  • Less turnovers lead to more field goals attempted and less points off turnovers against (e.g. fast break points).

It is important to take into consideration that more possessions per game usually means more assists and more turnovers. But picking this ratio as a measurement is more representative than picking e.g. the average assists or average points per game. The reason is that there are teams who are more offensive oriented and others who are more defensive oriented, so the comparison between two teams that don’t have the same performance in offense and defence could probably lead to inaccurate conclusions.

To begin with, there will be a comparison between the assist – turnover ratio of all 17 title winners and the competition average during that season and display their deviation. Are they close to average or not? If not, how much do they differ?

It is evident that only in 5 out of 17 cases the deviation was between -10% and +10%. Three times it was less than -10% and nine more than +10%. Furthermore the average deviation of all 17 champions from the competition average is 16,13%. Besides that, it is interesting to point out the following :

  • Kinder Bologna in 2001 and Barcelona in 2003 were approximately 15% below average.

  • Maccabi Tel Aviv in 2005, CSKA Moscow in 2008 and Real Madrid in 2015 were more than 50% above average.

To gain a clearer idea of the above chart, two charts will be displayed : The first one demonstrates the rank of each champion among all title contenders regarding the assist to turnover ratio.

And the second one the rank once more, but this time regarding the winning percentage (bear in mind that the total number of competing teams per season has already been mentioned). In case of tie – breaking, Pythagorian expectation calculator is used, based on points scored and points against.

The aim is to find the correlation between those two factors. Pearson’s correlation formula for a sample is used to achieve the above. It is a measure to tell how well two sets of data are related. The result will be between -1 and +1 and the closer it is to those two values, the stronger is the positive or negative correlation.

Let variable X be the rank concerning assist to turnover ratio and Y concerning the winning percentage. The dataset contains 17 values. Mean value of X is 7.529 and mean value of Y is 2.176 . The formula’s result shows that the correlation coefficient is approximately +0.167 . This means that those variables have a low positive relationship : as this ratio is better than the rest of the teams, the chance of be crowned champion at the end of the season is slightly increased.

What is therefore the final conclusion? That the assist to turnover ratio usually has to do with the success of a team, but not that much. Even though a better ratio and a position within the top – 5 in this stats category amongst all contenders usually increases the chance of winning the trophy, it is not always the rule. The probability of doing so and being less than 10% above competition average is 47%, a number which is not bad at all.


Bibliography

  1. Rawbw.com. (2017). Pythagorean Method. [online] Available at: http://www.rawbw.com/~deano/helpscrn/pyth.html [Accessed 10 Oct. 2017].
  2. Statistics How To. (2017). Pearson Correlation: Definition and Easy Steps for Use. [online] Available at: http://www.statisticshowto.com/what-is-the-pearson-correlation-coefficient/ [Accessed 10 Oct. 2017].
  3. Euroleague.net. 2017. Statistics. [online] Available at: http://www.euroleague.net/main/statistics. [Accessed 22 September 2017].
  4. Basketball-reference.com. 2017. Standings and Stats. [online] Available at: https://www.basketball-reference.com/euro/euroleague. [Accessed 22 September 2017].

*Main image from www.euroleague.net

I believe that sports and data science interrelate perfectly and their combination gives us the chance to understand better the whole science behind a single game. Therefore, for me Statathlon is a unique project that enables me to use my passion for research and writing about an important aspect of my life.

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