Analytics can be aimed at any measureable variable.  We use a variety of techniques to quantify control and ability in golf.  A simple short game analysis of carry versus total distance yields a rule of thumb for each golfer and their wedges to know what percentage of the distance left must be "carried" with the shot.  But deeper than this the R-squared correlation coefficient gives us a window into how consistent a golfer is with this ratio in the desired range we are testing (in this case carry distances of 7-25 yards).
       
     
 By converting Trackman data from distance traveled and distance from the target line into an angular dispersion value, we can start to use the 1 SD angular deviation as a measure of consistency with a particular club.  An improvement in 0.5 a degree equates to and improvement of 2.5 feet in the radius of the corresponding circle containing 66% of the golfer's shots.  Shorter distances to the target correspond to improvement in likelihood of holing out and these small improvements can be seen over many rounds of golf.  Round to round variability is thus something to be ignored unless a clear trend to suggest a root cause emerges.  One can use this type of big data approach to "trust the process" and not worry so much about individual shots, individual holes, individual rounds or even individual tournaments.    
       
     
     Dispersion as delineated by 1 standard deviation of angular deviation of a group of shots with various clubs, can be measured and plotted before and after a particular intervention or "treatment."  In this case, difficult balance poses were administered to two of the golfers shown in this graph while the other two "layed fallow." Can you guess which two golfers performed the exercises?   
       
     
 Analytics can be aimed at any measureable variable.  We use a variety of techniques to quantify control and ability in golf.  A simple short game analysis of carry versus total distance yields a rule of thumb for each golfer and their wedges to know what percentage of the distance left must be "carried" with the shot.  But deeper than this the R-squared correlation coefficient gives us a window into how consistent a golfer is with this ratio in the desired range we are testing (in this case carry distances of 7-25 yards).
       
     

Analytics can be aimed at any measureable variable.  We use a variety of techniques to quantify control and ability in golf.  A simple short game analysis of carry versus total distance yields a rule of thumb for each golfer and their wedges to know what percentage of the distance left must be "carried" with the shot.  But deeper than this the R-squared correlation coefficient gives us a window into how consistent a golfer is with this ratio in the desired range we are testing (in this case carry distances of 7-25 yards).

 By converting Trackman data from distance traveled and distance from the target line into an angular dispersion value, we can start to use the 1 SD angular deviation as a measure of consistency with a particular club.  An improvement in 0.5 a degree equates to and improvement of 2.5 feet in the radius of the corresponding circle containing 66% of the golfer's shots.  Shorter distances to the target correspond to improvement in likelihood of holing out and these small improvements can be seen over many rounds of golf.  Round to round variability is thus something to be ignored unless a clear trend to suggest a root cause emerges.  One can use this type of big data approach to "trust the process" and not worry so much about individual shots, individual holes, individual rounds or even individual tournaments.    
       
     

By converting Trackman data from distance traveled and distance from the target line into an angular dispersion value, we can start to use the 1 SD angular deviation as a measure of consistency with a particular club.  An improvement in 0.5 a degree equates to and improvement of 2.5 feet in the radius of the corresponding circle containing 66% of the golfer's shots.  Shorter distances to the target correspond to improvement in likelihood of holing out and these small improvements can be seen over many rounds of golf.  Round to round variability is thus something to be ignored unless a clear trend to suggest a root cause emerges.  One can use this type of big data approach to "trust the process" and not worry so much about individual shots, individual holes, individual rounds or even individual tournaments.    

     Dispersion as delineated by 1 standard deviation of angular deviation of a group of shots with various clubs, can be measured and plotted before and after a particular intervention or "treatment."  In this case, difficult balance poses were administered to two of the golfers shown in this graph while the other two "layed fallow." Can you guess which two golfers performed the exercises?   
       
     

    Dispersion as delineated by 1 standard deviation of angular deviation of a group of shots with various clubs, can be measured and plotted before and after a particular intervention or "treatment."  In this case, difficult balance poses were administered to two of the golfers shown in this graph while the other two "layed fallow." Can you guess which two golfers performed the exercises?