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Moth class sailing, three boats

Building Analytics of the Moth Nationals using Tableau Public edition

  |   Events

At the recent Moth UK National championships we decided to have a go at presenting various facts about the event and the class using Tableau Public .  This is a great viral marketing idea by Tableau and we’ve certainly had fun learning this limited, lightweight version of their popular Business Intelligence software.

 

The Moth class is one of the leading extreme singlehanded racing classes within modern sailing, with rules that let designers go wild and test sailor’s skills to the limit.  Constructed from carbon fibre the boats weigh around 30kgs in total and run on hydrofoils.  These are effectively aeroplane wings under the water which generate lift allowing the boats to fly above the water at speeds of up to 32 knots (36mph).

 

James has competed in the class for a number of years, finishing 26th at the last world championship in Italy.  At this year’s UK National Championships we decided to visualise some key pieces of information.  Here James talks through some of the findings and whether they yielded any insights.

 Whilst relaxing ashore after another 3 hard races during this years National Championships the usual musings cropped up during conversations with other competitors.  “He / She went well in that race, they are relatively light”.  “That type of hydrofoil seems to dominate in less than 15 knots of breeze”.  “Sail X seems to be the best when its windy”.  These are the kind of questions that are often asked at Moth events but rarely answered with a great deal of rigour.  A great deal of science goes into the design of the boats using CFD software & other tools, analysing data on results & performance however usually receives little beyond anecdotal attention.

 

Given we had some time on our hands around racing and I was keen to learn more about using Tableau Public collecting some data for analysis seemed like a good idea.  So after racing in true ‘lean’ fashion one evening an entry list was passed around in the bar for competitors to, as honestly as possible, fill out their weights in KGs.  The hunger for data then set in and we started working out what other data points we could easily capture about the event.  Soon we had peak and average windspeeds for the races, results, equipment breakdowns and other stats.  All free, easily collectable and potentially quite insightful information if presented and analysed in the correct fashion.  So what did we find out about this unique class?

 

Firstly there doesn’t appear to be a significant correlation between age and results.  Does this mean experience can offset youth and fitness to a degree?  Perhaps older sailors have bigger budgets and are able to afford the latest, faster and more reliable equipment?  Lots of food for thought, however with an age range of 14 to 55 years of age the Moth class has to be at the more ‘age inclusive’ end of the sailing spectrum despite its extreme nature.

 

Its been a common view outside of the class that you have to be lightweight to compete.  Within the class there is also a common perception that the heavier sailors perform better in windier conditions due to having more leverage when hiking (hanging off the side…) thus consistently keeping more power in the sail. Conversely in lighter wind conditions heavier sailors (myself included…) will often complain that its difficult to match the pace of lighter sailors, partly due to lighter sailors helping the boat to rise onto the hydrofoils earlier.  Overall when compared against performance there doesn’t appear to be a significant correlation during the week between results and weight.  The top 25 overall featured an impressive range of 89 KGs down to 67 KGs.  Perhaps due to the wide range of conditions (races held in winds ranging from 16-28 knot averages) both the heavier ‘dogs’ and the lighter had their day during the event?

 

Looking at the three windiest races (4-6) there is a slight concentration between 73 and 86 KGs within the top 25 finishers which would go someway to supporting the hypothesis that heavier sailors perform better in these conditions.  Looking at the three lightest races (3,10 & 11) however there’s less of a correlation.  The sub 70 KG sailors certainly didn’t ‘walk away with it’ and there is still a cluster between 80 and 70 KGs in the top 25.  Perhaps technique or equipment are more important factors here?  Its possible to look at equipment using the summary statistics tab to try and shed some more light on this.

 

Looking at equipment breakdowns and average results for the top 25 overall in the 3 windiest and lightest races there are some interesting results.  The Exocets seem fast both in the windy and the light conditions, the Mach2 performed slightly better in the windier races and the Ninja (a boat normally famed for light wind results) performed better in the windier races.  The largest performance difference for the rocket was due to retirements in the windy races.  Sail choice is slightly harder to judge due to competitors changing sails depending on conditions, however from the data we have the North seems to be a more consistent sail than the 16.  Whether this is because those with more ability use it or it is a performance advantages for those competitors is debatable.

 

Boat design

Average result in 3 Lightest races Average result in 3 Windiest races
Ninja 21.4 17.2
Rocket 12 37
Exocet 10.56 10.59
Mach2 19.57 17.87
Sail design
KA MSL16 14.21 17.38
North GM1 8.4 6.27

 

There’s a clear correlation, unsurprisingly, between consistency (range of results) and overall results.  There are also however some notable exceptions for the week worth highlighting.  Mike Cooke & Ben Paton proved to be 2 anomalies in this area – despite being 2 out of the 5 most inconsistent competitors in the regatta they bucked the trend and still both managed to finish in the top 12 overall – quite a feat.  Ollie Holden also possibly earned the title of Mr Consistent with an overall result of 25, no retirements and a spread of just 9 points between his worst and best results – very impressive.

 

Overall its been quite interesting visualising this data in Tableau Public – it has the odd frustrating feature but generally it’s a good tool to use.  Tableau’s viral marketing approach seems to be yielding benefits as its spreading like wildfire particularly within journalism.  There were some new insights that I believe we gleaned from the data, it was also however useful for backing up existing understanding using hard facts and figures.