MY SYSTEM \u00b7 STEP 2 \u2013 ANALYSES<\/span><\/p>\n<\/div> Having data is half the battle. Here I show how 5,000 strokes turn into reliable answers \u2014 with \u03c3-consistency, Strokes Gained, and median instead of gut feeling.<\/span><\/p>\n<\/div> Collecting data is easy. Understanding data is the art \u2014 and for senior golfers, it's a different discipline than for tour pros. We have less swing speed, more dispersion, and our daily form fluctuates more. Anyone who works with averages here will make the wrong decisions.<\/p>\n For over three years, I've distilled three principles from 4,063 range shots and 50 Excel columns that guide every one of my analyses\u2014from a small range block to an 18-hole evaluation.<\/p>\n Dispersion over mean. Median instead of best. Strokes gained over score.<\/strong> Once these three sentences are in place, the analysis is half done.<\/p>\n<\/div><\/div><\/div><\/div><\/div> An 8-iron with a median of 130 yards and a standard deviation of 6 yards is better than one with a median of 138 yards and a standard deviation of 14 yards. Consistency beats distance \u2014 almost always. In senior golf, where the shot lands matters, not how far it would go with a perfect strike. could<\/em>.<\/p>\n<\/div>\n<\/div><\/div><\/div> With \u201emy best drives go 220m,\u201d I misplay a hole. With \u201emy median driver goes 175m,\u201d I play realistically. Senior golfers have more bad shots than perfect ones \u2014 the statistics must reflect that, not ego.<\/p>\n<\/div>\n<\/div><\/div><\/div> \u201eGross 51 on 9 holes\u201d says little. \u201eSG -2.23 vs. Scratch, PW as biggest loser with \u03a3 -2.90\" says everything. Mark Broadie's method separates luck from skill - and makes every round comparable to any other.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div> Standard deviation of carry distance. My I8 has \u03c3 7.53 m \u2014 best value in the bag between 80 and 120 m. Low = club self-plans.<\/p>\n<\/div>\n<\/div><\/div><\/div> Standard deviation of the lateral deviation. Says more about water hazards than any distance statistic. During the driver breakthrough on June 9: \u03c3B halved from 23 m to 8.9 m.<\/p>\n<\/div>\n<\/div><\/div><\/div> The median is more robust against outliers than the average. My driver median: 175 m (best 220 m, average is misleading). Plan with the median, not with the best result.<\/p>\n<\/div>\n<\/div><\/div><\/div> Mark Broadie's Gold Standard: How many strokes do I gain or lose per stroke vs. scratch baseline. Best diagnostic measure \u2014 Wendlohe C 12.06.: SG \u22122.23, PW \u03a3 \u22122.90 as the biggest score killer.<\/p>\n<\/div>\n<\/div><\/div><\/div> How often do I save par when I miss the green in regulation? That reveals the strength of my short game. Senior Tour pros average 55 %; I\u2019m aiming for 40 %\u2014my Achilles\u2019 heel in 2026.<\/p>\n<\/div>\n<\/div><\/div><\/div> What percentage of greens do I leave after three or more putts? The most honest lag-putting stat. My Wendlohe pattern: 3-putts pile up on first putts 4\u20138 m \u2014 this led to the P5 test.<\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div> I do that after every range session and after every round\u2014routine, not a project.<\/p>\n<\/div>\n<\/div><\/div><\/div> R10-CSV or Bebrassie-PDF go into the Master Excel. 50 columns, one row per shot - Date, Club, Distance, Side, Spin, Surface, Drill, Note. A central table for everything<\/strong> is the prerequisite for any comparability.<\/p>\n<\/div>\n<\/div>\n Pivot table with the six KPIs from the block above \u2014 n, \u03c3L, \u03c3B, Median, Strokes Gained, Scatter Ratio. Excel formulas do that. As soon as I add new strokes, all KPIs update automatically.<\/p>\n<\/div>\n<\/div>\n Comparison tab in the master Excel with traffic light logic: red for deviation from target, green for hit. Target values from two sources \u2013 Tour reference (TrackMan 2024) and realistic HCP brackets for players 60 and over. Without a benchmark, no number is meaningful.<\/strong><\/p>\n<\/div>\n<\/div>\n Not all weaknesses are equally important. A driver problem costs 1.5 strokes per round, a 3-putt percentage costs 4 strokes. Score-Impact in Strokes per 9 Holes<\/strong> is my sorting criterion. What costs the most comes first.<\/p>\n<\/div>\n<\/div>\n From the diagnosis, it becomes precise one<\/em> Hypothesis for weekly plan: \u201eIf I train PW distance control with Drill W1, \u03c3L will decrease from 14m to under 10m in 4 weeks.\u201d Measurable, datable, testable. This moves us to Step 3 \u2014 Training after analysis<\/em>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div> This is how an analysis really went \u2013 from the brassie export to the week's training focus.<\/p>\n<\/div>\n<\/div><\/div><\/div> Raw data<\/strong> Bebrassie-Export 9-hole round, 51 gross strokes (+15). First reaction: \u201eWell, it was windy.\u201d Wrong reaction. The right reaction was: look at the data.<\/p>\n Bat Breakdown:<\/strong> The par-3s were step-by-step the biggest score killers with a cumulative -2.90 strokes over the round. Worse than the driver, worse than any putt. Three approaches from 80-100m ended up 12-18m short or long \u2014 which is geometrically unavoidable with a stroke length of 14m.<\/p>\n Score-Impact Prioritization:<\/strong> Weakness in putting costs 2.9 strokes per 9 holes, 3-putts cost 1.8 strokes. The driver is in the green zone \u2014 no training focus needed. So the order was clear: first wedges, then lag putts.<\/p>\n Training Hypothesis W25:<\/strong> If I practice 30 putts per session, 3 times a week, from 80 meters with distance control (target: 8-meter circle) and practice 2 sets of lag putts from 4\u20138 meters, I can reduce my PW \u03c3L to under 11 meters in 3 weeks and my 3-putt rate to under 25 %. Measurement: Pre\/post-test in W25 and W28.<\/em><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>How to analyze<\/span><\/h1>\n<\/div>
Three principles to guide any senior golf analysis<\/h2>\n
Dispersion before mean<\/h3>\n
Median instead of best<\/h3>\n
Strokes Gained instead of Score<\/h3>\n
What I really measure per club<\/h2>\n
Length dispersion<\/h3>\n
Page scattering<\/h3>\n
Median Carry<\/h3>\n
Strokes Gained<\/h3>\n
Scrambling-Quote<\/h3>\n
3-Putt Rate<\/h3>\n
In Five Steps from Raw Data to Training Hypothesis<\/h2>\n
Pull raw data into the master<\/h3>\n
Calculate KPIs per bat<\/h3>\n
Compare with target values<\/h3>\n
Prioritize weaknesses by score impact<\/h3>\n
Derive training hypothesis<\/h3>\n
Runde Wendlohe Course C, June 12, 2026<\/h2>\n
\nSG vs. Scratch: \u22122.23 strokes
\nFiR (Fairway in Regulation): 57 % (25 % the previous day)
\nGiR (Green in Regulation): 11 %
\nScrambling: 0 % \u2014 one missed green approach lost<\/strong><\/div>\n
\nPW \u03c3L Soll: \u2264 10 m \u00b7 IST: 14 m \u2192 ROT<\/span>
\nDriver SD: \u2264 15 m \u00b7 IST: 8.9 m \u2192 GREEN<\/span>
\nTarget 3-Putt Rate: \u2264 15 % \u00b7 Actual: 33 % \u2192 ROT<\/span><\/div>\nThree Excel files that carry everything<\/h2>\n