enCYCLONEpedia: Weighing the Four Factors

lg tm 4 factors update 2 19 12

By Kirk Haaland of enCYCLONEpedia.comFollow Kirk on Twitter @Khaal53

I am very, very far away from being the originator of the “four factors of basketball” or anywhere near that but it is the basic premise of a lot of the basketball data that I have been gathering and analyzing this year.

If you’re a novice the simplest place to read about the four factors is here.  In short, the factors are effective field goal percentage (eFG%), turnover percentage, offensive rebounding percentage (keeping in mind that the inverse of your opponent’s offensive rebounding percentage is your team’s defensive rebounding percentage), and free throw rate (FTR) (FTM/FGA (some use FTA/FGA but I like to use FTM to also include the variable of how well your team makes their free throws)).

Through relentless studies — or so we’re told — it has been determined that the four factors from above are weighted in importance to determining the outcome of any game like this:

  1. Effective Field Goal Percentage, 40%
  2. Turnover Percentage, 25%
  3. Offensive Rebounding Percentage, 20%
  4. Free Throw Rate, 15%

Always the curious and wandering mind of things lacking overall importance, I wanted to see how all of this panned out.  Through two methods, I embarked upon my journey.

I used this during football season as well and thought I’d once again bore you with it as best as I could, but, do you remember our good friend, the correlation coefficient?  I took all 70 games that have been played so far in Big 12 play and ran the coefficient to see which had the strongest relationship to the outcome of games.

The coefficient calculates “r” and runs on a scale from -1 to 1.  As the number approaches 1 the likelihood for a statistical correlation increases and as it approaches -1, the likelihood for an inverse correlation increases.  The closer the number is to zero, it is less likely that the numbers have a statistical link.

Generally, if the value falls between -0.3 and 0.3 the relationship is considered weak.  From 0.3 to 0.7 or -0.3 to -0.7 the relationship is considered moderate.  Anything from 0.7 to 1 or -0.7 to -1 is considered a strong relationship.

Instead of only using the calculation of the four factors, I also included the opponent’s four factors, and the margins of those factors for the coefficient.  I also threw in points scored per possession and points allowed per possession as well as a few random numbers like three pointers attempted, three point percentage, assist percentage (percentage of made baskets that came off of an assist), and steal percentage (percentage of opponent’s possessions with a defensive steal).

In the chart every state with a “T” in front of it is the focus team and “Opp” is obviously the opponent…

bball corr

Right away at the top, you’ll notice eFG% margin, for obvious reasons, it accounts for not only a team’s good shooting but their opponent’s poor shooting as well.

I do find it interesting that in Big 12 games, there is a stronger correlation to winning by scoring high points per game than there is to holding your opponents to as few points per game as possible (yes, I know that the team that scores the most points wins, but I think you follow what I’m trying to say…I hope).

You can look over the numbers as you go down while remembering that a negative number means that as that stat gets “better” it correlates stronger to losing.

One other notable is just how low in correlation a team’s offensive rebounding percentage has been.  The opponent’s offensive rebounding percentage has a stronger link so you’d have to assume that the real number is somewhere in the middle but it’s interesting to me that the offensive rebounding percentage margin is below that of free throw rate margin.

Then, my second method…

I looked at each of the four factor’s margins on a game by game basis to see what records they produced when team’s had the advantage.

When team’s finished in the positive on…

…eFG% à 56-14 (0.800), when the margin is greater than 2.5% à 48-3 (0.941)

…OReb% à 36-24 (0.514), when the margin is greater than 10% à 23-16 (0.590)

…TO% à 46-21 (0.687), when the margin is greater than 5% à 29-11 (0.725)

…FTR à 51-19 (0.729), when the margin is greater than 0.02 à 46-15 (0.754)

Again, eFG% is by far the most important factor but is curiously led by previously considered 4thmost important factor of free throw rate by winning percentages alone.

If you’re like me the next question is which two factors lead to winning more than the others, am I right?  So, I went ahead and ran each possible combination of two out of the four factors when a team was positive in both (as with above, anything that was a margin of zero was thrown out) and came up with this when both factors are in the positive…

…eFG% & TO% à 35-3 (0.9211)

…eFG% & FTR à 42-5 (0.8936)

…TO% & FTR à 32-4 (0.8889)

…eFG% & OReb% à 26-4 (0.8667)

…TO% & OReb% à 18-5 (0.7826)

…OReb% & FTR à 27-10 (0.7297)

The sample size is surely much smaller than the rigorous historical studies done that determined the weights of each of the four factors in the first place but it is still interesting to me how much more important it is to get to the free throw line and make them than it is to have a better offensive rebounding percentage.

Further, there have been 28 games where a team has had a positive margin on eFG%, FTR, and even or better on FTR and in those games those teams are 27-1. The lone loss was Oklahoma State falling to Baylor at home where they were barely positive on all three of those factors and got crushed on offensive rebounds.

That is the short version of analyzing the four factors and their real life importance thus far in Big 12 play.  Next up, to pull all of this together and finally wrap it up is a look at how each team has performed in each of these areas through their first 14 conference games.  Per the usual, the shading goes from red (the better the performance) to blue (the worse the performance) except for in possessions per game where the shading goes from red (most—fastest) to blue (least—slowest).

tm 4 factors update 2 19 12 550x168

Obviously, the most important thing is outscoring the opponent so there is naturally more red at the top and blue at the bottom of the first shaded section since the data is sorted by conference win percentage.  Given the exercise we went through previously it is a similar case for the eFG% section.  While the shading of the TO% section and FTR section is roughly the same the randomness of the OReb% section affirms what we did above (keeping in mind that this chart is combined data for the conference season and the discussion from earlier was game by game so there are a few differences but we’re basically talking about the same thing).

It isn’t a new or novel concept but it does put some facts behind the common thought of the weight of each of the four factors above.  I’m sure that there is some variance from conference to conference style of play and it is important to remember that this was done by purely looking at Big 12 games.  In the end, if you want to win games you better make shots and make it hard on your opponent to do the same.  This year’s Big 12 is revealing that is still the case while free throw prowess seems to be a better conducer to success than that of rebounding or even turnovers.

The Cyclones do a few of these things very well…they are the best or near the best in the conference in limiting the opponent’s trips to the free throw line, limiting opponent’s offensive rebounds, keeping their own turnovers down, and they have the third best eFG% in the Big 12.  Those are the primary keys on a laundry list of reasons that the Cyclones find themselves in contention for a third place finish in the Big 12 and a trip to the NCAA tournament.


Kirk Haaland


Kirk has been a contributor at Cyclone Fanatic since the fall of 2009 and is a lifelong Cyclone fan. He eventually started his own website,, where he cultivated an interest in statistical analysis and historical Iowa State football and basketball data. In 2014, Kirk came to Fanatic and housed his works here. In 2015 he launched a new website,, as the co-founder. There you can find in depth analysis of all things involving advanced statistical analysis in college football for every FBS program. Kirk graduated from Iowa State University in 2006 with a degree in Industrial Technology and has worked as a Manufacturing/Quality Engineer ever since. He's married to his wife, Kelley, and has three daughters, Hannah, Hayley, and Kinley (plus his Golden Retriever, Clyde).