How to Win a Moth World Championship

Stefanie Schurer∗

School of Economics and Finance

Victoria University of Wellington

and

Institute for the Study of Labor (IZA)

Abstract

What matters for success in Moth : skills, training eort, money, weight or intelligence? In this article we demonstrate that skills and training eort are the two single most important determinants of success in high performance . If you want to win the 2012 Moth World Championship, better know how to gybe foiling and have clocked up many days on any boat the year before. If all else fails, buy the right boat and be happy.

JEL classication: L83.

Keywords: High performance sailing, Moth sailing, investment, boat equipment, and skills.

∗Address for correspondence: Victoria University of Wellington, School of Economics and Finance, Wellington 6041, New Zealand. Tel: +64-4-4636708, Email: [email protected]. Acknowledgment

The author would like to express her gratitude to a number of people without whom this project could not have been realised. Special thanks go to Scott Babbage for granting me access to weight and registration data, and for making the survey a requirement for registration. Also special thanks go to Peter Moor for providing me with wind data from every race, which unfortunately could not be used in the end. I am particularly grateful to the Swedish Sailing Team, i.e. Emma Aspington, Peder Arvefors, Magnus Gravare, and Martin Gravare for brain-storming with me on, and helping out to design a survey instrument. Last, but not least, I would like to thank all 94 sailors who took the eort and time to ll out the questionnaire and to allow us to weigh them.

1 1 Introduction

The 2011 Moth World Championship (MWC) took place on 6-14 January 2011 and was hosted by Belmont 16' Sailing Club, Belmont, NSW, under the authority of NSW, Australia. The racing took place on Lake Mcquarie, a treacherous piece of water. At the outside it looks like a peaceful holiday resort, but once sailing on the water makes clear that this lake is a perfect training and racing turf. Heavy winds, unpredictable gusts, and frequent wind changes are on the daily menu, so are the odd visits of bull sharks and hammer heads. It is no surprise that Belmont is home to many Olympic and Blue Water Racing sailors. The 2011 MWC had a record of 109 sailors competing in the races. To learn more about what determines success and high performance of Moth sailors during a regatta, we decided to collect information about the sailors' eorts to prepare for the MWC, their skill endowment, and equipment. Such data was collected via a survey that the sailors were asked to ll out and return to the race organisers. The survey was distributed during registration (6-7 January 2011) as part of the race kit collection. Measurement of weight was conducted at the actual registration desk. The regatta organisers brought along a medium-priced household bathroom scale and asked every sailor to step on the scale when they picked up their race kit. The survey was designed to get the following information from the sailors: (1) How much they trained in the last year on the Moth or other sailboats; (2) How much money the spent on Moth sailing; (3) Whether they can do foiling tacks or gybes; (4) What type of boat they sail, and which mast and sail brands they own; (5) How t they are; (6) How much work they do on their boats; and (7) How much Moth and race experience they have. We also added a series of fun questions to lighten up the survey, and test whether any of these fun facts correlate with actual performance. The fun questions were: (1) How happy are you with your life; (2) What do you think is your IQ; and (3) Do you think that Scott Babbage will nish seventh. We also asked the sailors in which position they expect to nish the regatta. This question allowed us to calculate the degree to which a sailor over- or under-estimates his or her performance and whether a large gap is predictive of performance. To best of our knowledge, no such data have ever been collected, and therefore we have little guidance in choosing the right set of questions or

2 to assess their validity. The survey instrument is displayed in Appendix A. The funniest or unusual comments, or unreasonable answers (16 in total) are displayed in Appendix B. The MWC was divided into a qualication and a medal series. During qualication, 9 races were held, and for each race two heats of about equal size took place. Which sailor sailed in which heat was initially randomly allocated. After every race sailors were re- allocated to dierent heats to balance the average performance in each heat, and to give every sailor the same probability of meeting any other sailor. The top 55 sailors in the nal ranking of the qualication series made it into the gold series, while the remaining sailors moved on to the silver series. In the gold and silver series 9 and 8 races, respectively, were conducted. We have data on each sailors points in each of the 18 (17) races they participated in, and their nal ranking after the qualication and medal races. We also have information on how often a sailor did not nish a race. Main focus of this article is to evaluate the factors which determine the average performance across the 9 qualifying or 8-9 medal races, and the number of times a sailor did not nish a race.

2 Basic characteristics of the sample

The basic descriptive statistics are displayed in Table 1, which lists the mean (or pro- portion if the variable is an indicator), standard deviation (Std. Dev.) if applicable, the minimum and maximum value, the number of individuals if the variable is an indicator variable, and the total number of observations for which we have non-missing values. Sum- mary statistics about boat-brands, mast-brands and sail-brands are displayed in Table 2. Out of the 109 sailors who competed in the MWC, 94 completed a survey. Out of these 109 sailors, there were only 5 women. This small number is surprising because the Moth dinghy is particularly well suited for lighter and nimble sailors, where women would have a comparative advantage. Two thirds of all competitors sailed under an Australian ag (72 sailors), 8.3 percent under a US-American ag (9 sailors), and 7.3 percent under a British ag (8 sailors). The fourth-largest group sailed under a Swiss ag (7 sailors). Smaller groups were Swedes (4 sailors), Japanese (3 sailors), and New Zealanders (2 sailors). One sailor each represented China, Singapore, Ireland, and Germany.

3 The average age is 34, ranging from 15 to 60. As it will turn out, age and experience is by no means associated with high performance in the Moth class. Many dierent boat brands were represented at the 2011 MWC, but the most common brand was a Mach 2 (61.2 percent or 58 boats). Mach 2 replaced the previously dominant Bladerider, which was the second most common brand (11.7 percent or 10 boats). More than 21 percent of all boats were brands such as Fastacraft (4), Assassin (2), Ninja (2), Prowler (2), or Lazich Axeman II (2). Six boats were home-built. About 30 percent of sailors owned a Mach 2 mast (27), 26.4 percent a CST mast (24), and 12 percent a McConaghy mast (11). However, about 8 percent of sailors reported to own two or more brands. For these sailors we do not know which mast they actually used during racing. Other masts owned by individuals sailors were Bladerider (3), Aardvark (1), Southern (4), or a home-built mast. One sailor owned and sailed with a wing mast, a latest development in ski and high performance racing. Less heterogeneity appears to be the case in the sail-brands: Almost 78 percent owned exclusively a KA sail (73). Only 3 sailors used exclusively a North sail. We asked the competitors how much money they spent on average per year (over the last three years) on Moth sailing. The average spent about AUS$14300, with the lowest investment being AUS$710 and the highest investment being AUS$50000. We think that the reliability of these numbers is questionable. First, 14 percent of competitors refused to answer the question. Second many sailors may have misunderstood our question by telling us the average money spent over three years. A new Mach 2 cost approximately AUS$20000, hence an average of AUS$14000 p.a. may be reasonable if a new boat was bought the year before. Spending AUS$50000 per year appears to be too large, but maybe it was meant as a sum over three years. Moth sailing is a sport for engineers and mechanics. Competitors admitted to have spent on average for every hour sailed three hours on boat work. Five sailors did not spend any time on boat work, while one sailor claimed that he spent 40 hours per hour sailed (Scalpel). The majority (51 percent) of sailors spent about 0.5-2 hours on boat-work per hour sailed. The largest amount of extra boat-work experienced sailors on home-built (12 hours) or Bladerider (4.2 hours) boats. The average reported boat-work for Mach 2s is 1.7 hours. On average, the competitors spent about 46 days Moth sailing in 2010, with a shocking

4 range from 0 days to 365 days a year. More than 50 percent of the sailors spent at least ten days training on other boat classes, with an average of 35 days per year. The average competitor has about 7 years of racing experience at the international level, ranging between 0 (about 23 percent) and 30 years (3 percent), and 6 years of Moth sailing experience. Measuring skills in Moth sailing is dicult, but since this dinghy is a foiling Moth, a best guess would be to proxy skills by the ability to master foiling tacks and gybes. We therefore asked the competitors what percentage of their tacks and gybes they could do foiling. Whereas on average the competitors claimed that they could gybe foiling in 75 percent of the time, they could only tack foiling in 11 percent of the time. We are aware that these self-assessments may be far o reality, however, as it will turn out, the percentage of foiling tacks and gybes are one of the strongest determinants of high performance in races. Another minimum criteria for high performance in Moth sailing is physical tness. We did not have an objective measure for tness, but we asked competitors whether they assessed their physical tness to be (1) very good or good, (2) average, or (3) bad or poor. More than 30 percent claimed to be in good or very good tness, 45 percent said they had average tness, and 25 percent conceded to be in poor or bad tness. The fun questions resulted, as expected, in a lot of measurement error in the answers and a lot of non-response. Also, many sailors felt invited to add extra comments to the questions. A full list of these comments is provided in Appendix B. Only 83 competitors answered the IQ question. The average reported IQ is 112, with a range of 65 to 201. Two individuals reported an IQ of 300 and 1000. We recoded these answers to missing, since there are no reports in the world of such high IQ. For comparisons, the highest recorded IQ on the Stanford-Binet score is 228, achieved by Marylin Vos Savant born in 1946. Lowest IQ scores are usually not recorded or publicly discussed, however, an individual with IQ of 70 would be considered as severely mentally challenged. Thus, technically speaking we should also set the answer provided by one sailor of 65 to missing. We did not do so, because without knowing an ocial lower bound, a value of 65 is theoretically possible. Our question about happiness attracted only two funny answers. One person claimed a happiness level of 1000, and another person reported a level of 11, although the upper scale

5 was set at 10. We left the value of 11 as it is, and recoded the value of 1000 to missing. The average reported happiness level is 8, which is slightly higher, but comparable to an Australian average of about 7.8 [See World Values Survey]. About 90 percent of all sailors report a happiness level of 7-10, and thus the majority of our sailors are a happy lot. The eight sailors who reported levels below 6 [possibly depressed] are 4 Mach 2 sailors, 1 Assassin sailor, 1 Prowler sailor, 1 Rocket Surgeon and 1 Scalpel sailor. We do not suggest that there is a causal link between the type of boat and happiness of a sailor, but it is surprising that not a single Bladerider is among the less happy sailors. The opinions were equally split about whether Scott Babbage would nish again in seventh place. 40 percent said yes, another 40 percent said no. 16 percent said maybe, and one sailor wrote that he didn't care whereas another sailor said that more important would be whether [Scott] will take Koshi for a hot lap. A small number of 4 sailors predicted that Scott would nish better. Scott Babbage did eventually nish better, in third position, and as a consequence he probably needed to nd a new sponsor for the 2012 MWC. We also asked competitors to predict their nal ranking. The data show that on av- erage sailors were little o between their predicted and their actual nish position (-1.3). This negligible average however is the result of an equal number of individuals who over- and under-estimated their performance. Only two sailors predicted their nal position correctly: The person who won the MWC and the person who came 25th. About one- quarter of the sailors over-estimated their performance by about 15 positions or more, and a similar number under-estimated their performance by 14 or more positions. Never- theless, about another quarter of sailors assessed their performance correctly by a margin of error of about 5 positions. Last, from the racing data we could also construct the number of races a sailor did not nish (DNF) a race across all 18 (17) races sailed during the regatta. On average, competitors did not nish four races during the regatta, however, the distribution of the DNF races is wide. According to Figure 1 about 30 percent of sailors (26) nished all races. Another 12 percent (10) missed out on only on one race, but still 13 percent missed out on 3 races. Only 9 sailors did not nish 10 or more races. Later, we will see that it is only a small number of factors that explain the number of DNF races.

6 3 What determines performance?

We measure performance in three dierent ways: (1) The nal position; (2) The average points per race in either the 9 qualication races, or the 8-9 medal races; and (3) The total number of races not nished during both the qualication and medal races. We rst show bivariate correlation plots between selected variables and the nal and the average performance. Then we move on to a multivariate analysis exploiting the longitudinal nature of our race data.

3.1 Bivariate analysis

3.1.1 Final position

Figure 2(a) shows a scatter-plot of the relationship between nal position and age. The individual dots represent the individual sailors observations and the number next to it represents the sail number under which the sailor sailed. The solid line is a tted value of the bivariate relationship. The steeper the gradient of that line, the more strongly the two variables are correlated. A downward-sloping line indicates a positive, an upward-sloping line indicates a negative relationship with performance.1 The light gray area around the tted line is a 95 percent condence interval. It indicates whether the relationship between the two variables is signicant in a statistical sense. The wider the condence interval, the less signicant is the relationship. Age and the nal position are only weakly correlated. Sailors ranked in the bottom 10 were on average ve years older than sailors in the top 10, but this dierence is not signicant. According to Figure 2(b) there is also no relationship between self-assessed IQ and nal position, the tted line is at. Figure 2(c) indicates a week positive relationship between happiness and performance, where sailors in the top 10 scored on average above 8, while sailors in the bottom 10 scored on average below 8. Yet again, the condence intervals are so wide to suggest that the positive relationship is due to randomness. It needs to be mentioned, however, once dropping the three outlier observations who reported happiness values of 3, then the positive relationship become stronger. Stronger relationships are found between the nal ranking and the number of days spent sailing on other boat classes, as can be seen from Figure 3(a). Sailors in the top 10

1Note that the larger the number of points the worse is the performance.

7 spent on average 80 days sailing on other boats in 2010 leading up to the MWC, while sailors in the bottom 10 spent on average no day sailing on other boats. This down-ward sloping relationship is signicant as the condence intervals between top and bottom 10 sailors are not overlapping. Spending more days Moth sailing is also positively related with nal ranking as shown in Figure 3(b). On average, the top 10 ranked sailors spent about 70 days in 2010 sailing Moth, while sailors ranked in the bottom 10 spent about 30 days on it. This relationship is not signicant, a seemingly surprising result. This paradox can be explained by the observation that many of the top ranked sailors are Olympians training for their other boat classes such as the (Nathan Outerridge and ) or (). Does spending more money result in a better ranked position? Figure 3(c) would suggest yes. Sailors in the top 10 spent on average AUS$17500 per year sailing the Moth, while sailors in the bottom 10 spent only AUS$8000 per year. This relationship is marginally signicant. We need to concede as well that in these bivariate plots we assume linearity between money spent and ranking. This assumption may be wrong, because the eectiveness of any extra dollar spent should be small at high expenditure levels, whereas it should be high at low expenditure levels. We allow for these non-linearities in the multivariate analysis. The right weight has often been discussed in sailing circles as a crucial element in high performance. Heavier sailors have an advantage in heavy winds, lighter sailors have an advantage in lighter winds. The wind during the MWC in 2011 was on the stronger side. During the rst four medal races, the wind ranged between 16 and 28 knots. In the fth race the wind ranged between 16 and 22, in the sixth and seventh race the wind was lower between 12 and 16 knots and between 8 and 15 knots, respectively. In the last two races the wind bounced around between 16 and 20 knots, and between 18 and 22 knots, respectively. Thus, heavier sailors should have had an advantage in the medal races. From Figure 4(a) we learn that there is no relationship between weight and nal ranking if pooling data from both gold and silver eets. This is so because of a large degree of heterogeneity in the link between weight and ranking across the two eets. Figure 4(b) reveals that there is a positive relationship between weight and ranking in the gold eet. Top ranked sailors weighed about 82 kg on average while bottom ranked sailors in the gold eet weighed about 75 kg, although the condence intervals are overlapping.

8 One reason for this is that three of bottom ranked competitors happened to be female sailors who weighed between 61 and 75 kg, and thus are at the lower end of the weight scale. On the other hand, the relationship between weight and ranking is reversed in the silver eet. Heavier sailors ranked worse than lighter sailors, as illustrated in Figure 4(c). We can only speculate on why this may be the case. So far, we have seen only weak correlations between ranking and some variables of in- terest. Stronger relationships exist between self-assessments of tacking and gybing skills and nal ranking. Figure 5(a) demonstrates that ranking depends crucially on the per- centage of successful foiling tacks. While the top 10 ranked sailors said that on average 25 percent of their tacks are done foiling, among the bottom 10 ranked sailors the average is 0. The condence intervals between the two groups are not overlapping. Even more signicant are the dierences between top- and bottom-ranked sailors with respect to their gybing skills, as demonstrated in Figure 5(b). While among the top 10 ranked sailors between 90 and 100 percent of all gybes are done foiling, this happens only about 40 to 50 percent of the time for sailors ranked in the bottom 10. This relationship is exceptionally strong and signicant. We will be able to conrm these results in the multivariate analysis. Last, we wanted to know how well the sailors were able to predict correctly their own nal ranking. Figure 5(c) shows the scatter-plot between predicted ranking (vertical axis) and the actual nal ranking. A 45-degree line leading from the origin to the right- hand corner of the graph would indicate a perfect match. Observations that lie above that 45-degree line indicate that the competitors were too pessimistic about their nal ranking (in fact they did much better), while observations below that line indicate that competitors were too optimistic (indeed, they performed worse). Only two sailors got it perfectly right (1st and 25th position). 51.7 percent of all sailors on whom we had data were too pessimistic about their nal ranking, whereas 46.2 percent of all sailors were too optimistic. However, only a small percentage of sailors got it really wrong, i.e. deviated by as much as 40 points (5.5 percent who were that pessimistic and another 5.5 percent who were that optimistic).

9 3.1.2 Average performance across medal races

In the following Figures 6 and 7 we show box-plots of the average performance of sailors across eight to nine medal races stratied by boat or equipment characteristics, country (ag sailing for), self-assessed physical tness, and opinions about whether Scott Babbage would nish seventh. The average performance is measured by points per race, where a higher number of points indicate a worse performance. A box plot describes the distribution of the points achieved per race. The gray, rectangular box indicates the interquartile range, which contains roughly 50 percent of the observations (75th percentile to 25th percentile). The white, vertical line represents the median, which is that observation which has exactly 50 percent of observations above and below itself. It is a good proxy of the average observation in the sample, and it has the advantage that it is independent of outliers. The whiskers indicate the range of all observations (minimum and maximum), and thus gives a good idea about the spread in the data. In some cases round bullets are displayed as well: these are outliers which indicate observations that lie extremely far away from the majority of the observations in the data. Here, they are dened as observations that lie 1.5 times the interquartile range above or below the bottom quarter or above the top quarter of all observations. Figure 6(a) shows the average number of points per boat brand. Sailors in Mach 2s or Scalpels show the lowest number of points on average, and therefore indicate the best performance. It appears that the spread of performance is worse for sailors in Mach 2s, in comparison to sailors in Scalpels. However, the distribution of the performance of Mach 2 sailors is based on 58 sailors observed over 9 races, whereas the distribution of the Scalpel sailors is based on one sailor observed over nine races. The median number of points for Mach 2 sailors is about 35, whereas it is about 70 for Bladerider sailors (11 sailors observed over 9 races). None of the other boats performed equally well than the Mach 2. Average performance by mast-brand is displayed in Figure 6(b). Unfortunately, we do no know which mast the sailors used during the race, we only know which masts they owned. As outlined above, there are about 8 percent of sailors in the sample who reported to own two or more mast brands. The average performance is the best for the ve sailors who own one mast of the brand KA. The second best average performance is of the wing sailor, but this average is constructed only over nine observation of one and the

10 same sailor. Generally, the performance by mast-brand is very spread out, and average performance is particularly low for those 3 sailors who sail with a Bladerider mast. With respect to sail-use and performance, Figure 6(c) illustrates that there are no simple answers to this question. 70 percent of all sailors own only a KA sail, but the average performance is not the best for this group. The highest average performance is observed for one sailor who uses a combination of KA and Hyde and for another sailor who uses a wing sail. However, what is not shown in this Figure is that every sailor in the top 10 nal ranking sailed with a KA sail. What are the variations in performance across countries? Note that the country does not necessarily indicate the nationality of a sailor. The average performance across 9 races was best for New Zealand sailors (2), and US American sailors (9), and by the Swedish (4) and Japanese sailors (3), as can be seen from Figure 7(a). What is not shown in the Figure is that seven out of the top 10 nishers of the regatta were from Australia, one from New Zealand, one from Switzerland, and one from the US. Figure 7(b) demonstrates unambiguously that higher levels of physical tness, al- though self-assessed, resulted in higher average performance. Nevertheless, the spread was equal across all levels of tness. Self-assessments of tness are a relative concept. For instance, two sailors in the top 10 nal ranking claimed that their physical tness is either bad or average, which clearly cannot be the case from an objective point of view. The sailor who assessed his tness as bad came second. Maybe if his tness had been as average as reported by the winner's tness, he may have won the regatta. What these self-assessments show is that they work on average, but they are not necessarily useful in explaining top performance. Last, we wondered whether an assessment of Scott Babbage's likely nal position is in any way linked to the average performance of a sailor. This question was initiated as a fun question to lighten up the survey. We asked whether Scott Babbage would nish seventh (as last year). Scott won sponsorship from Channel 7 (an Australian TV channel) and that made the question interesting. In fact, three sailors commented in the surveys on exactly this point. Four sailors predicted that Scott would do better than seventh, but these four sailors performed on average the worst. The best average performance over 9 races is linked to a sailor who showed little sense of humor at various instances of his survey, and additionally claimed that he did not care. The average performance was

11 better for sailors who doubted that Scott would come seventh, in comparison to sailors who thought maybe or yes.

3.2 Multivariate analysis

So far, we have presented bivariate correlations between performance and variables of interest. This is restrictive, as many of the variables under consideration are likely to correlate strongly with each other. We therefore performed a multivariate analysis ex- ploiting the longitudinal nature of the data. We observed 94 sailors on whom we have survey data over up to 18 races. In order to not to lose too many observations we recoded the missing values on Weight and Money spent on Moth sailing to 0 and agged these observations with a dummy variable. Therefore, the estimation sample consists of 93 sailors, or 837 sailor-race observations for the analysis of the qualication series and 793 sailor-race observations from the medal series. We estimated a model in which the number of points a sailor achieved in a race is regressed on the following variables: (1) Being a female (Dened as a dummy variable that takes the value of 1 for yes, and 0 for no), (2) Weight in kg, (3) Percent of tacks foiling; (4) Percent of gybes foiling; (5) Boat brand (Base: Mach 2, dummy variables for Bladerider, Other brand, Home-built); (6) Physical tness (Base: Very good or good, dummy variables for average tness and bad or poor tness); (7) Money spent on Moth sailing: (Base: Lowest quartile of distribution, dummy variables for highest, medium, and low levels); and (8) a dummy variable for being in gold eet for the sample of medal races. Initially, we included polynomials of age, racing experience, number of days sailed the Moth, or other boats, hours spent working on the boat, life satisfaction, and IQ. However, none of these variables was statistically signicant at any conventional level of signicance. We think that many of these variables strongly correlate with the tacking and gybing skills, tness, and the type of boat sailed. The model is estimated with a linear random eects approach which accommodates the period-to-period correlation of the error terms of the model due to the fact that we observe the same sailors (who have some xed characteristics which we do not observe) in every race. Such models allow for sailor-specic variations in performance across races, but assumes that these sailor-specic variations are independent of all other included variables.

12 3.2.1 Qualication series

Table 3 reports the estimation results for the sample of the qualication series. Note, that in each race there were two heats with 55 and 54 sailors respectively. The dependent variable is number of points achieved in a race, and is bound between 1 (winner) and 56 (did not nish the race). Overall, the included variables explain about 53 percent of the variation in points per race. The important determinants of the number of points are the tacking and gybing skills, and the boat types. Their estimated coecients are statistically signicant and of meaningful size. Sailors who would move from never gybe while foiling to perfectly mastering foiling gybes scored on average almost 18 points lower in the race (p-value 0.007). Sailors who would increase their foiling tacks from 0 to 30 percent reduced their average points by 5 (calculated as -15.426*0.3). A 100 percent on foiling gybes and a 30 percent on foiling tacks represent the average ability of the top 10 ranked sailors. Similarly strong eects, but in the opposite direction, are observed for sailors on a Bladerider, a home-built, or on any other brand boat in comparison to sailors on a Mach 2. The eects are staggering. A sailor on a Bladerider, on average scored 17 points higher per race than a sailor on a Mach 2. Sailors on any other brand or a home-built boat scored on average 16 and 18 points higher than a sailor on a Mach 2. The eects across the three dierent boat types vis-a-vis a Mach 2 are not dierent from each other (p-value of a Wald-test of equality of the three coecients is 0.878). Hence, it does not matter which other boat you sail: you will perform less well than a Mach 2 by about 17 points per race. Average and bad or poor physical tness is also associated with higher points per race in the magnitude of 1.5 to 2.7 relative to sailors who reported good or very good tness. The eects are not signicant, though. Money spent on Moth sailing during the qualication series is not statistically signicant either. Weight is also not statistically signicant. Female sailors tend to score slightly higher than male sailors per race (about 2.3 points), but the dierence is also not signicant.

13 3.2.2 Medal series

To assess the average performance in the medal series, we pool the observations from the gold and the silver eet. To adjust the points achieved by sailors in the silver eet, we added 55 points to each sailors score. Thus, sailors in the gold eet obtained points between 1 (winner) and 56 (DNF), whereas sailors in the silver eet received points between 1 (winner) and 55 (DNF). After the recoding, sailors in the silver eet scored between 56 and 110 (DNF). The same linear random eects model (see above) is estimated, and the estimation results are displayed in Table 4. The dependent variable is bound between 1 (winner) and 110 (DNF). In total we have 793 sailor-race observations. Overall, the variation of all included right-hand-side variables explains about 79 per- cent of the variation in points achieved per race. This is a very good t of the data. There are some surprising and some expected results worth mentioning. First, mastery of foiling tacks and gybes are yet again singled out as the most important determinants of scoring low in a race. Sailors who move from never foiling during gybes to always foiling, reduce their score by almost 13 points. This eect is signicant at the 1 percent level (p=0.007). Similarly, sailors who move from never foiling during a tack to 30 percent of the time foiling (this is the level of the top 10 nishers), reduce their score by 3.4 points (-11.3*0.3). This eect, however, is only signicant at the 10 percent level (p=0.091). Hence, gybing skills appear to be more important than tacking skills to do well in the medal races. A surprising result is that Bladerider boats no longer under-performed on average the Mach 2 boats. On average, they still score 3.8 points higher in the medal race than Mach 2 boats, but the eect is no longer statistically signicant (p=0.387). In contrast, other boat brands and home-built boats score on average 11 and 13 points higher than Mach 2 boats. This nding is due to the fact that only three other boat brands made it into the gold nal, and thus by denition, the majority of these home-built and other boat-brands always score 55 points extra in every race. The three other brands which made it into the gold nal were a Ninja, a Scalpel, and a home-built boat. Also, only two Bladerrider boats made it into the gold nal. In the medal races, money spent appears to be an important factor: Sailors who were in the highest quartile of money spent on Moth sailing (approx. A$32000 per year) scored

14 on average 9 points lower than sailors who were in the lowest quartile of money spent (approx. AUS$2721). Hence, a dierence of almost AUS$30000 improves the score by 8.3 percent (9/109). This eect is signicant at the 5 percent level (p=0.041). Female sailors scored on average 6 points higher than comparable male sailors, but again, this eect is not statistically signicant. The insignicant gender eects are ex- pected because the sample comprises only 5 women. Average physical tness is associated with almost 5 extra points scored in each race in comparison to someone who says he or she had good or very god tness. This eect is still not signicant, but closer to a signicance level of 10 percent (p=0.129).

3.2.3 Number of times a sailor did not nish a race

Last, we investigate the determinants of the number of times a sailor did not nish (DNF) a race. The distribution of this dependent variable is shown in Figure 1. A third of all sailors (N=26) nished all 17 or 18 races, and 25 percent of all sailors failed to nish 1 to 3 races (N=16). Only one sailor failed to nish 16 (out of 17) races. Not nishing a race is a relatively common phenomenon among Moth sailors. One reason for not nishing is that some part of the boat breaks, which makes foiling impossible and forces the sailor to withdraw. Due to the count nature of the dependent variable (0, 1, 2,...) we estimate a negative binomial count model. The explanatory variables are the same as those described in Section 3.2, except for including also a variable labelled positive illusion. This variable is constructed from the dierence between the sailors actual and expected nish position. The larger the gap between the two the greater is the illusion of the sailor about his or her sailing skills, and or the strength of his or her boat. Positive Illusion is dened to take the value 1 if a sailor over-judged his or her performance by at least 15 nal position points, and 0 otherwise. Table 5 reports the estimated coecients for a sample of 91 sailors. Overall, we obtained very similar eects as for the models on the number of points a sailor achieved. The more often a sailor gybes foiling the smaller is the number of races not nished. The dierence between sailors who always and never gybe foiling is about 3 races not nished. No statistically signicant dierence is observed for foiling tacks (p=0.282). Bladerrider boats failed to nish 4 races more than Mach 2 boats (p=0.000), and

15 home-built boats failed to nish 5 races more than Mach 2 boats. A very strong eect is observed for sailors who had a positive illusion about their performance. Sailors who over-judged their expected nish by 15 or more points failed to nish 3 more races than sailors who did not. This eect is highly statistically signicant (p=0.002). We can only speculate on how to explain this phenomenon. On the one hand, it is possible that sailors who over-judge their performance are too optimistic or over-condent which makes them less attentive to detail and preparation and less focused during races. Such behaviour could lead to a higher probability of failing a race. In contrast, it may simply be that a sailor was well prepared but experienced unexpected material failure on his boat. One such example is the accidental collision of two boats, caused by another sailor's lack of focus. Further information would be needed to learn about why a race was abandoned. Money spent on Moth sailing is not associated with the number of DNF races. Women have a smaller number of DNF races than comparable men, but the negative eect is small and not signicant. Fitness is also not related to the number of DNF races.

4 Conclusion

What do you need to do to perform well in the next Moth World Championship? To have the highest probability of a top 10 nish you should be able to master foiling gybes all the time and foiling tacks at least in 30 percent of all cases during qualication and the medal races. Your weight is not that important overall if the wind is comparable to Belmont. However, if you make it into the medal series, it would probably pay o to weigh around 80 kg. From the data it appears to be imperative to sail a Mach 2 (with a KA sail and KA mast), if you want to nish at least in the top 30. Generally, it would help if you had sailed about 60 days on the Moth and 80 days on any other boat to make it into the top 10. Nevertheless, the benets of this amount of training appear to be linked with a higher probability of mastering foiling gybes and tacks. The good news is that money spent on the Moth sailing, over and above sailing a Mach 2, does not substantially improve your performance, neither does your level of intelligence. It would help if you were a little bit happier and physically t than the rest of the sailors, as this appears to be the common theme among top 10 nishers. Being funny, i.e. writing entertaining answers into a survey, unfortunately does not do the trick.

16 Table 1: Summary statistics of survey data

Variable Meana Std. Dev. Min Max Indiv. All Obs.b

AUS 0.661  0 1 72 109 CHN 0.009  0 1 1 109 GBR 0.073  0 1 8 109 GER 0.009  0 1 1 109 IRL 0.009  0 1 1 109 JPN 0.028  0 1 3 109 NZL 0.018  0 1 2 109 SIN 0.009  0 1 1 109 SUI 0.064  0 1 7 109 SWE 0.037  0 1 4 109 USA 0.083  0 1 9 109 Age 34.268 10.14 15 60  93 Female sailor 0.046  0 1 5 109 Weight in kg 70.294 23.946 0 101  109 Fitness: Bad or poor 0.245  0 1 23 94 Fitness: Average .447  0 1 42 94 Fitness: Good or very good 0.309  0 1 29 94 Number of days spent Moth sailing in 2011 45.714 55.054 0 365  91 Number of days sailed other boats in 2011 34.703 66.211 0 300  91 How many years have you sailed the Moth 5.801 8.371 0 40  94 How many years have you raced on international level 6.962 7.932 0 30  93 Percent of all tacks that are foiling tacks 0.11 0.208 0 0.9  93 Percent of all gybes that are foiling gybes 0.749 0.317 0 1  93 Money spent on Moth sailing on average 14307.418 11829.173 710 50000  81 Hours worked on boat for every hours sailed 3.154 6.846 0 40  89 Self-assessed IQ 111.807 25.837 65 201  83 Self-assessed happiness 8.035 1.665 1 11  91 Number of times did not nish a race during Worlds 3.872 4.146 0 17  109 Number of races not nished (Medal race) 2.495 2.62 0 8  109 Number of races not nished (Qualication series) 1.376 1.957 0 9  109 Dierence between expected and actual nish position -1.209 28.185 -109 64  91 If Actual - Expected > 14 0.253  0 1 23 91 If Actual - Expected < -13 0.264  0 1 24 91 If |Actual - Expected| < 6 0.231  0 1 21 91

Note: a Mean is the average value of a continuous variable and a proportion of an indicator variable (0,1 values). b All observations refer to non-missing values in survey or registration data. The total number of sailors competing in the Moth World Championship in 2011 was 109 sailors. 94 lled out a survey, but some sailors did not respond to some questions in the survey.

17 Table 2: Summary statistics of boat-, sail, and mast-brand

Variable Percentage Std. Dev. Min Max Indiv. All Obs.a Boat-brands Mach 2 61.7  0 1 58 94 Bladerider 11.7  0 1 11 94 Own-built 6.38  0 1 6 94 Other boat brands 20.21  0 1 19 94 Breakdown of other boat brands Assassin 2.13  0 1 2 94 Fastacraft 4.26  0 1 4 94 Gilmoor 1.06  0 1 1 94 Goddard 1.06  0 1 1 94 Hungry Tiger 1.06  0 1 1 94 Lazich Axeman II 2.13  0 1 2 94 Lister 1.06  0 1 1 94 Thorpe 1.06  0 1 1 94 Ninja 2.13  0 1 2 94 Prowler 2.13  0 1 2 94 Rocket Surgeon 1.06  0 1 1 94 Scalpel 1.06  0 1 1 94 Mast-brands Aardvark 1.1  0 1 1 91 Bladerider 3.3  0 1 3 91 Bubble 1.1  0 1 1 91 Burvill 1.1  0 1 1 91 C-Tech 1.1  0 1 1 91 C-Tech/CST 1.1  0 1 1 91 CASTRA 1.1  0 1 1 91 CST 26.37  0 1 24 91 CST/Mach 2 2.2  0 1 2 91 CST/McConaghy 1.1  0 1 1 91 CST/Thorpe 1.1  0 1 1 91 Fastacraft 1.1  0 1 1 91 KA 5.49  0 1 5 91 KA/CST 1.1  0 1 1 91 Mach 2 29.67  0 1 27 91 Mach 2/CST 1.1  0 1 1 91 McConaghy 12.09  0 1 11 91 Southern 4.4  0 1 4 91 Wing 1.1  0 1 1 91 Own-built 3.3  0 1 3 91 Sail-brands Avalon 1.06  0 1 1 94 Bladerider 1.06  0 1 1 94 Fastacraft 1.06  0 1 1 94 Hyde/KA 1.06  0 1 1 94 KA 77.66  0 1 73 94 KA/Hyde 1.06  0 1 1 94 KA/Kessler 1.06  0 1 1 94 KA/North 2.13  0 1 2 94 KA/TRUFLO 1.06  0 1 1 94 Kasseler 1.06  0 1 1 94 North 3.19  0 1 3 94 North/Rapter 1.06  0 1 1 94 Quantum/KA 1.06  0 1 1 94 Raptor 2.13  0 1 2 94 Truo 1.06  0 1 1 94 UK Halsey 1.06  0 1 1 94 Wing 1.06  0 1 1 94 Own-built 1.06  0 1 1 94

Note: a All observations refer to non-missing values in survey data. The total number of sailors competing in the Moth World Championship in 2011 was 109 sailors. 94 lled out a survey, but some sailors did not respond to some questions in the survey.

18 .3 N=26 .2 Proportion N=11 N=10 .1

N=6 N=6 N=5 N=4 N=4 N=3 N=2 N=2 N=2 N=2 N=1 N=1 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 16

Figure 1: Number of times sailors did not nish a race during regatta (qualication + medal races)

19 3653 60

2 3689 3098 3520 3666 3793 50 3787 3801 3792 9364 1 3735 9233 33163798 3783 3658 3295 3638 3795 3841 3731 3781 3657 3264 3637 3260 40 3636 9346 3803 37703797 3767 3178 3707 9318

Age 3771 3820 3777 3700 6 3794 36413654 3634 36103711 3738 3271 53 3622 3615 3715 3685 3659 3573

30 7 3656 3592 3288 3676 3784 3655 9336 3757 3772 35963713 3639 3786 3580 3750 3800 9260 9360 3209 4 3232 3775 3774 3420 3773 3776 3623 3379 3315 20 3719 3464 3572 3717 3865 1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values age

(a) Age

3776 3264 200

6

3641 3795 150 2 3596 3666 3784 3700 3738 3209 7 3592 3654 936435803767 3178 5 3520 IQ 3639 3787 36563774 37193757 3771331637983379 3271 3841 3295 37973865 3573 9233 3750 3623 93183717 3098 3770 3637

3773378637314 367638203 363837153735379237773636 342037933775379436223658 32883655 3801 32603653 9346 36899336 100

9256 3659 1 3783 36853657 3610 3464 35729260 3803 50 1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values Self−assessed IQ

(b) What do you think is your IQ?

3464 11

3750363936346 3731 3820 3735 37193657 3775 3315 35729260 9256 10

9 3773 4 378737153800 2 37933794362336583772 93603288 38013713 36533654 3573 3295

8 7 3786 1 53 37833638 3792366637773232363637743784 375734203592361036223711 37383798 3795 36413271377037973264 3767 384131783689 3316

7 3676 3685 3700 931837713717 3209 3098 936493463637 3520 9336

6 3655 3865 3596

5 3656 3379 3659 3580 4 Life satisfaction

3 3776 3260 2

1 3803 0 1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values Self−assessed happiness

(c) How happy are you?

Figure 2: Correlation of nal ranking with age, IQ, and happiness

20 320 3750 3786 3800 3623 280 240

3776 200 160

120 37736 4 36153803 9360 3209 3717 80 3787 3622 7 5 36383735 3774 3713 3784 3798 40 3 3783 3738 3098 3572 3637 3639 3757 35923610 3801 3271 3767 3634 3676 37923666 3707 379533153596 37973264 3580 36562 371937003420 3770 9260 36899336 0 13820 3781 3715 377732323636 3657 379337753794371136589318377237713316337932883655 34643659326036533654 3865936493463573925635203841923331783295

Days spent sailing on other boats last year 1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values Daysoth

(a) Days spent sailing last year

3232 360 300

240 3623

3731 3636 3717 180

3750 3676 120 7 6 2

4 3719 Days spent moth sailing 3781 37353666 3420 3771 37679256

60 3794 3639 5 3715 3774 3757 3592 3711 3738 37703264 3776 3793 3795 3653 3689 36343786 1 3615 37833787 3656 3707 3772 3655320938013098 9364358035733637 35203841923331783295 38203803 37923777 37003775 36223658 3798 3596 32713654 3865 9346 9336 3773 3 3638 9318 331693603315 36593260 35723797 3685 3610 3379 3641 9260 0 3800 3784 3713 1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values Days spent moth sailing

(b) Days spent Moth sailing last year

3777 3623 50000

3795 3750 3792 40000 3676 3573

36393773 3717 3655

30000 3771 3786 37763820 3738 3641 3800 3638 7 3622 3772 3841 1 3803 3801 3572 Investment

20000 3615 3735 3793 331532093713 3271 3580 5 3794

4 378137833787 3666 3774 370034203775 3658 9364 3 3715 3784 3711 3316379833799360 3098 10000 3656 3767 36593653 9346 3689 3657 3464 37973264 9256352031789336 3634 9233 323236363685 3610 9318 32603654 9260

0 6 373137093769 37298 2 37333719375737073592 93323706 357437793288 3596 3770358438653661 3637936536053652 32953579 1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values Money spent on moth sailing on average

(c) Money spent on Moth sailing per year

Figure 3: Correlation of nal ranking with training eort and money spent on Moth sailing 21 3573 100

3654 3738 9346 3520 3657

90 3820 3658 3605 3676 3781 3777 3786 3798 9364 3841 3773 3729 3656 3420 37133098 9365 3659 3767 6 3793 32603653 9256 3769 3771 9233 3750 37093776 3 3865 3634 363837873735 2 3772 3801 80 3731 3666 3271 3783 3636 3795 3641 7 5 3622 9318 9260 3639 4 371538003792 3775 3316 3288 36893579 3700 3178 3803 8 3774 3209 3652 Weight 3615 3232 3757 3379 3572

70 3596 3770 3580 1 3685 9336 3623 37843719 9360 3464 3797 3592 3655 3661 3706 3794 3315 60 3610

3717 50 1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values Weight

(a) Weight

3657

90 3820 3658 3676 3781 3777 3786 3773 3729 3656 3420 6 3793 3769 3750 3709 3776 3 3634 36383787 3735 2 80 3731 3666 3783 3636 7 5 3622 3639 4 371538003792 3775 3700 3803 8 3774

Weight 3615 3232 3757 70 1 3685 3623 3784 3719 3592

3794 60

3610 1 11 21 31 41 51 Final position

95% CI Fitted values Weight

(b) Weight in gold eet

3573 100

3654 3738 9346 3520

90 3605 3798 9364 3841 37133098 9365 3659 3767 32603653 9256 3771 9233 3865 3772 3801 80 3271 3795 3641 9318 9260 3316 3288 3689 3579 3178 3209 3652 Weight 3379 3572

70 3596 3770 3580 9336 9360 3464 3797 3655 3661 3706 3315 60

3717 50 56 66 76 86 96 106 Final position

95% CI Fitted values Weight

(c) Weight in silver eet

Figure 4: Correlation of nal ranking with weight

22 1

6 .9

4 .8

3634 3657 .7

3 .6 3719 3773 36763615 36233711 3209 .5

3232 .4 3776 .3

37573420 3379 Percent of Tacks .2

7 3803 3792365637743784 3700 9360365535963713 .1 2 3639 37863731 5 3787 3707 3795 3636 3717 3781 3735 3610 3658 3771 3572 3767

0 3750 13820 378336383715380036663777 3685 37933775359237943622 931837723738331637983288331538013464365930983260364136533271365437703797326492603865936435809346357336379256352038413178368993363295

1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values Percent of all tacks that are foiling tacks

(a) Percent of tacks foiling

1 37507 36346 37314 1 53 3656 37193707 37943623 3464 377636153781 3715 2 3657 3713 3767 3639 37923666 37743784 375734203775 36223711 3772 3288 3596 32603271 37733786 3787 37773636 3700 93183717 36553795 37973264 3841 .9 3232 3659 38203803378336383735 3592 377133169360 9260 .8 3738 3098 3572 3676 37933610 3658 .7 3178 3315 3653 .6

3209 36413654 3865 9346 .5 3379

3685 .4 Percent of Gybes .3

3801 3770 3637 .2

3580 .1 3798

0 3800 9364357392563520368993363295 1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values Percent of all gybes that are foiling gybes

(b) Percent of gybes foiling

6 3803 9364 3685 9318 35803573 3689 3295 111

3798 34643098 3770 92603865 934692563520 9336

101 3641 3637 91 3572 3738 32093801 3271 81 3771 3260 3772 3316 71 3288 3653 3820 35923794 9360 3797 3178 61 3658 3654 3795 3776 37353800 3232 37753610 51 3781 3784 3636 34203793 36223711 3655 3659 3841 41 3792 3717 3783 3719 3676 3777 3623 3379 3713 3264 3767 31 3656

Expected final position 3715 3700 3786 1 3615 3787 365737573707 3596 21 3774 36393773 3666 2

11 5 7 363437314 3

1 3750 1 11 21 31 41 51 61 71 81 91 101 111 Final position

95% CI Fitted values In what place do you think you finish in the Worlds?

(c) In what place do you think you will nish?

Figure 5: Correlation of nal ranking with tacking and gybing skills and nishing expec- tations 23 Assassin Bladerider Fastacraft Gilmoor Goddard Hungry Tiger Lazich Axeman II Lister Mach 2 Mark Thorpe Ninja Prowler Rocket Surgeon Scalpel own

0 50 100 Points per race

(a) Boat brand

Aardvark Bladerider Bubble Burvill C−Tech C−Tech/CST CASTRA CST CST/Mach 2 CST/McConaghy CST/Thorpe Fastacraft KA KA/CST Mach 2 McConaghy Southern Wing own

0 50 100 Points per race

(b) Mast brand

Avalon Bladerider Fastacraft Hyde/KA KA KA/Hyde KA/Kessler KA/North KA/TRUFLO Kasseler North North/Rapter Quantum/KA Raptor Truflo UK Halsey Wing own

0 50 100 Points per race

(c) Sail brand

Figure 6: Box-plots of performance during gold (9) and silver eet (8) racing by boat characteristics 24 AUS

CHN

GBR

IRL

JPN

NZL

SIN

SUI

SWE

USA

0 50 100 Points per race

(a) Country

average

bad

good

poor

very good

0 50 100 Points per race

(b) How would you rate your current physical tness

Better

Dont care

maybe

no

yes

0 50 100 Points per race

(c) Do you think that Scott Babbage will nish seventh?

Figure 7: Box-plots of performance during gold (9) and silver eet (8) racing by country, tness level, and Babbage's nish expectation 25 5 Estimation Results

Table 3: Estimated points in each race during the qualication series

Variable Coe SE t-stat p-value

Female (0,1) 2.304 4.433 0.520 0.603 Weight in kg -0.184 0.118 -1.570 0.117 Percent Tacks foiling -15.426 4.738 -3.260 0.001 Percent Gybes foiling -17.516 3.303 -5.300 0.000 Bladerider (0,1) (Base: Mach 2) 16.875 2.971 5.680 0.000 Other brand (0,1) 15.639 2.607 6.000 0.000 Home-built (0,1) 17.617 4.534 3.890 0.000 Bad or poor tness (0,1) (Base: Good/very good) 2.719 2.757 0.990 0.324 Average tness (0,1) 1.547 2.252 0.690 0.492 Highest level of money spent on Moth (0,1) (Base: Lowest) 0.215 3.267 0.070 0.948 Medium level (0,1) 3.635 3.172 1.150 0.252 Low levels (0,1) 2.074 3.030 0.680 0.494 Constant 47.759 9.990 4.780 0.000

Individuals × Races 837 Number of individuals 93 Nr heats 2 R-squared 0.531

Note: The dependent variable (DV) is the points gained in each of the 9 races. Higher values are associated with a lower ranking. The DV is bounded between 1 (highest ranking) and 56 (Did not nish). For each race there were two heats. Initial allocation into heats is based on random assignment. Subsequent allocation into heats is based on performance with the aim to randomise the heats again, so that every sailor has the same probability of competing against any other sailor. The model is estimated with a linear random eects model. All reported coecients are interpreted as the number of point-changes for every unit increase of the respective variable.

26 Table 4: Estimated points in each race during gold and silver eet racing

Variable Coe SE t-stat p-value

Female (0,1) 5.980 5.981 1.000 0.317 Weight in kg -0.043 0.159 -0.270 0.788 Percent Tacks foiling -11.275 6.666 -1.690 0.091 Percent Gybes foiling -12.732 4.734 -2.690 0.007 Bladerider (0,1) (Base: Mach 2) 3.789 4.377 0.870 0.387 Other brand (0,1) 11.149 4.005 2.780 0.005 Home-built (0,1) 12.750 6.410 1.990 0.047 Bad or poor tness (0,1) (Base: Good/very good) 2.979 3.727 0.800 0.424 Average tness (0,1) 4.658 3.067 1.520 0.129 Highest level of money spent on Moth (0,1) (Base: Lowest) -9.012 4.414 -2.040 0.041 Medium level (0,1) -2.892 4.289 -0.670 0.500 Low level (0,1) -2.340 4.094 -0.570 0.568 Gold eet (0,1) -47.104 3.477 -13.550 0.000 Constant 91.919 13.489 6.810 0.000

Individuals × Races 793 Nr of individuals 93 R-squared 0.786

Note: The dependent variable (DV) is the points gained in each race during the gold or silver eet racing, where 9 races were conducted for the gold eet and 8 races for the silver eet. Higher values are associated with a lower ranking. The DV is bounded between 1 (highest ranking) and 112 (Did not nish). The model is estimated with a linear random eects model. All reported coecients are interpreted as the number of point-changes for every unit increase of the respective variable.

27 Table 5: Determinants of number of times a boat did not nish the race

Variable Count SE t-stat p-value X¯

Female (0,1) -0.643 1.006 -0.640 0.523 0.044 Weight in kg 0.003 0.025 0.120 0.907 73.297 Percent tacks foiling -1.316 1.224 -1.080 0.282 0.113 Percent gybes foiling -2.950 0.650 -4.540 0.000 0.750 Bladerider (0,1) (Base: Mach 2) 4.272 1.163 3.670 0.000 0.110 Other brand (0,1) 2.823 0.920 3.070 0.002 0.198 Home-built (0,1) 5.251 2.184 2.400 0.016 0.066 Bad or poor tness (0,1) (Base: Good/Very good) 0.194 0.687 0.280 0.777 0.253 Average tness (0,1) 0.560 0.643 0.870 0.384 0.440 Highest level of money spent on Moth (0,1) (Base: Lowest) 0.011 0.556 0.020 0.984 0.242 Medium level (0,1) 0.790 0.700 1.130 0.259 0.187 Low level (0,1) -0.914 0.642 -1.420 0.155 0.198 Positive illusion about nal positiona (0,1) 2.763 0.900 3.070 0.002 0.253

Number of observations 91

Note: The dependent variable (DV) is the total number of races not nished during the regatta. It is calculated over 18 races for sailors that entered the gold eet and for 17 races for sailors who entered the silver eet. The variable is a count between 0 and 16 times. The distribution of the DV is shown in Figure 1. Illusion is calculated as the dierence between actual nishing position and expected nishing position (recorded before the regatta started). The variable positive illusion takes the value 1 if a sailor over-judged his or her performance by at least 15 position points, and 0 otherwise. This model is estimated with a negative binomial count model. The reported marginal counts are evaluated at the mean values of all covariates X¯ , which are reported in column 6. Each marginal count is interpreted as the change in races not nished if the respective variable changes by one unit.

28 Appendix A: Survey instrument

1. Birth date

2. Gender

3. Height

4. Boat brand or builder

5. Year built

6. What is the brand of your sail

7. Mast maker

8. How many days have you spent Moth sailing in 2010: [Number of days]

9. How many days have you spent sailing 2010 in any other class than Moth? [Number of days]

10. Please estimate: How many hours of boat work do you do for every hour sailed in the Moth: [Hours]

11. How much money have you spent on average each year on Moth sailing in the past three years (boat, equipment, competition, etc)

12. How many years have you sailed the Moth? [Years]

13. How many years have you raced on international level? [Years]

14. What do you think is your IQ? [Pick a number between 70 and 200] (an average in the population has 100, genius 150, and 70 Retard).

15. Of all tacks during a race, what percentage can you do as foiling tacks? [%]

16. Of all gybes during a race, what percentage can you do as foiling gybes? [%]

17. How would you rate your current physical tness for Moth sailing? [Bad, poor, average, good, very good]

18. All things considered, how happy are you? (0 [totally unhappy] to 10 [ecstatic])

19. In what place do you think you will nish in the Worlds [pick a number between 1 [First] and 115 [Last]]

20. Do you think Scott Babbage will nish seventh?

29 Appendix B: Funny comments from sailors written on survey instrument

1. Do you think Scott Babbage will nish seventh? Answer:

• Sailor 1: But more importantly: Will he take Koshi for a hot lap? • Sailor 2: No, I will • Sailor 3: In every race • Sailor 4: Of course, he wants to keep his sponsor • Sailor 5: Yes, because of sponsor requirements • Sailor 6: Don't care

2. What do you think is your IQ? [Pick a number between 70 and 200] (an average in the population has 100, genius 150, and 70 Retard). Answer:

• Sailor 1: 100, but stubborn as a mule • Sailor 2: Equal to a Pallet Jack • Sailor 3: Nathan Outerridge + 10 • Sailor 4: 1000! • Sailor 5: 65

3. How much money have you spent on average each year on Moth sailing in the past three years (boat, equipment, competition, etc). Answer:

• Sailor 1: 30K. I don't get KA discounts like Scott, Nathan, John H....

4. All things considered, how happy are you? (0 [totally unhappy] to 10 [ecstatic]): Answer:

• Sailor 1: WTF • Sailor 2: 9 Since I stopped working • Sailor 3: 5.679 • Sailor 4: Today 3, maybe tomorrow 8

30