Annual Handicap Review
Prepared by Peter Austerberry, CONGU
Presented by Liz Gaertner, CONGU
April 2013
Annual Handicap Review
AHR Process
Computer Model
AHR Report
Summary
Frequently Asked Questions
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
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?
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”
• Researchers have subsequently demonstrated, using the
large amount of actual data now gathered, that at a given
handicap for the better half of their scores, golfers’ scores
mirrored the “perfect golfer” very closely
Computer Model
• 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.
• 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
40.0
MGD
35.0
30.0
31.0
Plot of Median Gross Differential and CONGU
Exact Handicap
25.0
20.0
15.0
10.0
0.0
1.7
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
16.0
17.0
18.0
19.0
20.0
21.0
22.0
23.0
24.0
25.0
26.0
27.0
5.0
-5.0
Handicap
-10.0
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)
The linear relationship linking Handicap and Median Nett Differential
approximates to:
MND = (0.237*H) + 1.5745
Computer Model
Effect of number of scores on expected precision
EH
8.0
11.4
15.5
18.5
26.5
MGD
11.5
15.3
21.0
24.3
33.9
Scores
Range
Min
Max
1
8.1
23.4
2
10.5 21.0
3
11.6 20.3
4
12.0 19.8
5
12.2 19.4
7
12.5 18.7
10
12.8 18.3
20
13.6 17.4
• If a 15.5 handicap player returns 2 scores they could
indicate a handicap anywhere between 10.5 and 21.0!
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
Example
• 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 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 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
AHR Report
• For handicap decrease the AHR considers for recommendation
all players with at least 3 Qualifying scores
< -5 .0
-4 .9
-2
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL
N O CH AN GE
• For handicap increase 7 scores or more was desirable to give
an adequate level of precision
< -5 .0
-2
-4 .9
< - 3 .0 -3
-1 S H OT
-2
-1 IDEAL + 1 + 2 + 3 > + 3 .0
N O CH AN GE
+ 4 .9
> + 5 .0
+ 1 S H OT
• Note: Any player whose handicap has come down over the
year is excluded from any recommendation for increase
+2
AHR Report
• In 2012 the AHR program was adjusted to recommend increases
for players who returned 3 or more Qualifying rounds
• Size of the “tolerance” reflects the lack of precision / confidence
inherent in having fewer scores available
S c o r e s IDEAL + 1 + 2 + 3
3
4 ,5
6
4 .0
N O CH AN GE
N O CH AN GE
N O CH AN GE
> 4 .0 > 5 .0
> 6 .0
AT
LEAS T 1
S TROKE IN CREAS E
Summary
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
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 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
FAQ
WHY ALLOW 3 OR MORE SCORES AND A “TOLERANCE” OF 3
FOR DECREASES WHEN 7 OR MORE SCORES ARE NEEDED FOR
INCREASES?
It was considered that Handicap Committees could readily assess
below- handicap performance, especially when taking into
account performance in other forms of competition
In the case of increases more confidence was considered
necessary, particularly in order to minimise the occasions where a
player given an increase immediately performs better than their
new handicap.
FAQ
IF YOU ARE CONFIDENT IN THE SYSTEM WHY NOT IMPLEMENT
THE CHANGES AUTOMATICALLY?
In some ways it would be better to apply the recommendations
and have the Committee over-ride them if necessary. This
should ensure more consideration of the players’ situation
Certainly, applying the changes automatically would be better
than not implementing a recommendation because “the player
won’t like to be put up”
FAQ
IF CONGU KNOWS A SCRATCH PLAYER “AVERAGES “ 2 STROKES
OVER HIS HANDICAP AND A 24-HANDICAPPER 7 STROKES OVER,
SURELY THE SYSTEM SHOULD BE CHANGED SO THAT A PLAYER
PLAYS ON AVERAGE TO THEIR HANDICAP?
This could be achieved by adjusting Buffer Zones and the size of
the incremental decreases for each Category
However if that were implemented, FOR THE SAME ABILITY AS NOW
a Scratch player would be off 2, a 15.5 handicapper would be off 21,
a 24 handicapper would be off 31 and so on
If one of the main complaints now is that the system is biased
towards the high handicapper, imagine how much worse this would
be!
FAQ
GIVING FULL DIFFERENCE FAVOURS THE HIGH HANDICAP PLAYER IN
MATCHPLAY. WHY DID CONGU GET RID OF THE ¾ ALLOWANCE?
Scratch player MND 2,
24 H/cap, MND 7
Based on AVERAGE performance the data shows that the Scratch player has a
5 stroke advantage over the 24 handicap player - EVEN WITH FULL
DIFFERENCE
FAQ
GIVING FULL DIFFERENCE FAVOURS THE HIGH HANDICAP PLAYER IN
MATCHPLAY. WHY DID CONGU GET RID OF THE ¾ ALLOWANCE?
Off ¾ handicap the 24 handicap player would play off 18 - effectively
increasing their average ND to 13 - Hardly equitable!
FAQ
THE MODEL IS FINE IN THEORY BUT IT DOESN’T APPLY AT MY CLUB.
HOW CAN A COMPUTERISED SIMULATION BEAR ANY
RESEMBLANCE TO REALITY?
Data was obtained from the CDH scores in 2011 / 2012 from Active
players of both (+1, 0, 1) handicaps and 24 handicap
For (+1, 0, 1): a plot of Gross Differential v score frequency
for 2,000 returns (Men)
For 24: a plot of Nett Differential v score frequency for 2,000
returns (Men)
The study confirms previous findings that gave confidence in the
robustness of the Model to reflect reality at all levels of handicaps
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap returns
from the CDH (Men)
Plot of GD v score frequency from 2,000 (+1, 0, 1) handicap returns
from the CDH (Men)
250
Score frequency as
predicted by model
200
150
100
50
0
-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Comparing Actual data to the Model shows a strong correlation
Plot of ND v score frequency from sample of 2,000 24 Handicap returns
from the CDH (Men)
Actual MND is 7.0
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
Annual Handicap Review
AHR Process
Computer Model
AHR Report
Summary
Frequently Asked Questions