1. February / March / April 2015 Handicap Workshop The Annual Review

2. AHR Process To ensure all players in the Club have a handicap that reasonably reflects playing ability –Not simply to ensure decreases are applied –In these days of ageing memberships the focus is far more on ensuring the handicaps of declining players are adjusted upwards Many committees, faced with looking at the handicaps of 300+ members, simply didn’t bother

3. AHR Process  How to look productively at the performance over the previous year of all players in the club?  How to standardise the approach to assessing each player’s performance?  It was concluded that the CONGU system had developed to the point where administration by computers was almost universal. Thus devising a computer program to carry out the AHR process would solve both issues  How to establish from their performance data that a player’s handicap reasonably reflects their playing ability?

4. Computer Model  Obtaining sufficient data from correctly handicapped players, to enable valid statistical conclusions, was a problem  However in 2003 Peter Wilson (English Golf Union) had developed a mathematical model that effectively produced the scores of the statistically “perfect golfer”  The model is based on an observation that irrespective of handicap, each round resulting in a given gross score contains a predictable number of gross eagles, birdies, pars, bogies, double-bogeys, triple-bogeys etc.

5. Computer Model  Using this information the program simulates hole-by-hole scores for 10,000 rounds so that, averaged over the 10,000 rounds, the hole-by-hole profile matches that for any given gross score  Nett Double Bogey adjustment is then applied to the scores and the resulting CONGU handicap is calculated

6. Note: a Scratch player has a MGD of 1.7 NOT 0.0 and a 24-handicap player has a MGD of 31 (ie. 2 and 7 over handicap respectively)

7. The linear relationship linking Handicap and Median Nett Differential approximates to: MND = (0.237*H) + 1.5745

8. Computer Model EH8.011.415.518.526.5 MGD11.515.321.024.333.9 ScoresRange Min Max 1 8.1 23.4 210.5 21.0 311.6 20.3 412.0 19.8 512.2 19.4 712.5 18.7 1012.8 18.3 2013.6 17.4 Effect of number of scores on expected precision If a 15.5 handicap player returns 2 scores they could indicate a handicap anywhere between 10.5 and 21.0!

9. AHR Report  The linear relationship is used to test whether each player has a handicap that represents their current ability  The variability of player scoring patterns, and the number of scores returned, affects the precision that can be expected from the computed result  This is recognised by building in a “tolerance factor” around the exact calculation. For 7 or more scores it was shown that this is + / - 3  Example: player (handicap 7.5) returns 11 scores +12, +6, +15, +10, NR, +9, +3, +12, +14, +9, +8

10. Example  The year-end handicap is their “current ability” and this is subtracted from their MGD to get the Nett of their Median Gross Differential. So here NMGD = 10 – 7.5 = 2.5  The player’s Median Gross Differential is determined by first arranging the scores in ascending order +3, +6, +8, +9, +9, +10, +12, +12, +14, +15, NR  The MND of the “ideally handicapped” 7.5 player is calculated: (0.237*H) + 1.5745 = (0.237*7.5) + 1.5745 = 3.35  Then comparing the Actual with the Ideal for this player: Actual - Ideal = 2.5 – 3.35 = - 0.85  So the player is well within the tolerance of + / -3 and can be considered correctly handicapped

11. AHR Report For handicap decrease the AHR considers for recommendation all players with at least 3 Qualifying scores For handicap increase 7 scores or more was desirable to give an adequate level of precision, amended in 2012 to recommend increases for players who returned 3 or more Q scores Size of the “tolerance” reflects the lack of precision inherent in having fewer scores available

12. HOW CAN A COMPUTERISED SIMULATION BEAR ANY RESEMBLANCE TO REALITY? For (+1, 0, 1): Gross Differential v score frequency for 2,000 returns (Men) For 24: Nett Differential v score frequency for 2,000 returns (Men) Data was obtained from the CDH scores in 2011 / 2012 from Active players of both (+1, 0, 1) handicaps and 24 handicap The study confirms previous findings that gave confidence in the robustness of the Model to reflect reality at all levels of handicaps

13. Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap returns from the CDH (Men)

14. Comparing Actual data to the Model shows a strong correlation

15. Plot of ND v score frequency from sample of 2,000 24 Handicap returns from the CDH (Men) Actual MND is 7.0

16. Plot of ND v score frequency from sample of 2,000 24 Handicap returns from the CDH (Men) Predicted MND is (0.237*24) + 1.5745 = 7.26

17. Summary  Proper application of the AHR process will assist in ensuring that all players in the Club have a handicap that reasonably reflects playing ability  The AHR report is based on a computer model that has been confirmed (using actual scores obtained from 2011/2012 CDH data) to reflect reality at all handicap levels  The AHR report will assess the handicap performance of players in more detail, more objectively and more efficiently than can be achieved manually by handicap committees  The report can only make recommendations based on the scores returned. The committee must consider the recommendations and take any other factors into account before applying changes

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