This is an except from my free “2015 TriRating Report“. If you’re interested in more information about the 2015 long-distance Triathlon season, you should definitely check it out!

Overall Money List

First, here is an overview of the races I have included in my money list:

Type	Description	Total Prize Money	# of Athletes
Kona	Ironman World Championship (Kona)	$ 650.000	20
Ironman	Full-distance WTC races (not including Kona)	$ 2.271.000	318
70.3 Champs	70.3 World Championship (Zell am See)	$ 250.000	20
70.3s	70.3 races (not including Champs)	$ 2.177.500	400
Challenge	Full-distance Challenge races (including Roth)	$ 360.750	83
Sum	All included races	$ 5.709.250	582

This does not include the $1 million prize that Daniela Ryf collected for winning the “Triple Crown”.

The next table shows the Top 20 athletes – both from the men and women – that have earned the most prize money in the 2015 calendar year from all the races listed above:

#	Name	Sex	Total Money
1	Ryf, Daniela	F	$223.000
2	Frodeno, Jan	M	$213.000
3	Kessler, Meredith	F	$86.000
4	Blatchford, Liz	F	$79.750
5	Raelert, Andreas	M	$77.750
6	Potts, Andy	M	$75.500
7	Joyce, Rachel	F	$73.250
8	O’Donnell, Timothy	M	$67.500
9	Sanders, Lionel	M	$66.500
10	Wurtele, Heather	F	$64.500
11	Van Vlerken, Yvonne	F	$63.250
12	Steffen, Caroline	F	$60.750
13	Don, Tim	M	$58.000
14	Jackson, Heather	F	$57.750
15	Swallow, Jodie	F	$57.500
16	Naeth, Angela	F	$55.000
17	Kienle, Sebastian	M	$52.500
18	Pedersen, Camilla	F	$51.750
19	Piampiano, Sarah	F	$49.250
20	Gossage, Lucy	F	$47.000

Meredith Kessler has made it into third spot without any money from the “big money races” in Kona or Zell Am See.

Ironman Money List

Here are the Top 15 money earners from Ironman races (excluding Kona):

#	Name	Sex	Ironman	Total	Overall Rank
1	Vanhoenacker, Marino	M	$44.000	$44.000	24
2	Van Lierde, Frederik	M	$35.000	$40.250	26
3	Kessler, Meredith	F	$34.000	$86.000	3
4	Van Vlerken, Yvonne	F	$33.250	$63.250	11
5	Hanson, Matt	M	$31.000	$32.000	40
6	Ryf, Daniela	F	$30.000	$223.000	1
6	Frodeno, Jan	M	$30.000	$213.000	2
6	Blatchford, Liz	F	$30.000	$79.750	4
6	Swallow, Jodie	F	$30.000	$57.500	15
6	Naeth, Angela	F	$30.000	$55.000	16
6	McKenzie, Luke	M	$30.000	$36.750	33
6	Monticeli, Ariane	F	$30.000	$35.750	35
6	Symonds, Jeff	M	$30.000	$31.250	43
6	Hauschildt, Melissa	F	$30.000	$30.000	44
15	Sanders, Lionel	M	$29.500	$66.500	9

70.3 Money List

Here are the Top 15 money earners from 70.3 races (including the 70.3 Champs):

#	Name	Sex	70.3 Total	Zell Am See	Other 70.3s	Overall Rank
1	Ryf, Daniela	F	$73.000	$45.000	$28.000	1
2	Frodeno, Jan	M	$63.000	$45.000	$18.000	2
3	Wurtele, Heather	F	$58.000	$20.000	$38.000	10
3	Don, Tim	M	$58.000	$-	$58.000	13
5	Kessler, Meredith	F	$52.000	$-	$52.000	3
6	Sanders, Lionel	M	$37.000	$-	$37.000	9
6	Tisseyre, Magali	F	$37.000	$10.000	$27.000	32
8	Steffen, Caroline	F	$33.750	$-	$33.750	12
9	Aernouts, Bart	M	$33.250	$10.000	$23.250	25
10	Potts, Andy	M	$33.000	$-	$33.000	6
11	Kaye, Alicia	F	$32.500	$7.500	$25.000	39
12	Goss, Lauren	F	$31.500	$-	$31.500	41
13	Swallow, Jodie	F	$27.500	$-	$27.500	15
13	Reed, Tim	M	$27.500	$-	$27.500	51
15	Boecherer, Andi	M	$27.250	$6.500	$20.750	36

It’s interesting to note that Tim Don and Meredith Kessler have almost made it to the top of the list without any money from the 70.3 Championships.

I have been publishing Race Predictions for a few years now, so it’s about time to have a look at how “good” my predictions are. When I started in 2011, there were quite a few changes in the algorithm and the parameters to deal with a number of edge cases. During 2015 there have not been any changes, so this is a good data pool for validation.

Data Used

In 2015 I have published predictions for 36 Professional Ironman-distance races, 31 Ironman-branded races by WTC and 5 more Challenge races.There have been a total of 1098 finishes, 688 by male athletes (62.7%) and 410 by females (37.3%). These were posted by 600 different athletes, 382 male (63.7%) and 218 females (36.3). In addition there were 349 DNFs, 244 by males (70%) and 105 by females (30%).

Using my algorithm and the available start lists, I have seeded the participants in each of the races, and predicted 930 finishing times (84.7% of all finishers). There are some cases when I didn’t predict the finishing times, for example when an athlete didn’t have any prior IM-distance finishes or when there was a late entry (and therefore the athlete not included in the start list).

Predicting the Winners

Here’s a look at the places the eventual race winners have been seeded based on previous results and the start lists:

With 36 IM-distance races, there are 72 winners (one each for the male and female race). My algorithm has correctly predicted the winner in 26 races (36%), and another 26 winners were seeded in #2 or #3 (winning frequency of an athlete seeded on the podium: 72%). Only three winners have been seeded higher than 8th: Kirill Kotshegarov was seeded 10th at IM Chattanooga, Mel Hauschildt was seeded 11th at IM Melbourne, and Matt Hanson was seeded 12th at IM Texas. There was also one unrated (and therefore unseeded) winner in 2015: Jesse Thomas won IM Wales in his debut Ironman.

The numbers would be even better when only considering the athletes that finished a race. Only including athletes that actually started increases the frequency of picking the right winner to 39% (and one of the podium picks to win the race to 80%), also discarding athletes not finishing would have yielded 42% and 83% of the winners.

Time Predictions

In my pre-race posts, the finish times are predicted for each athlete that has raced an Ironman race before. The algorithm considers the previous finishing times of an athlete and the course that the race is going to be held on.

The following graph compares the actual finish times to the predicted finish times (each data point is one dot on the graph). Dots towards the upper left are results where the actual finish was faster than predicted, dots towards the lower right are results that are slower than predicted.

The graph shows actual and predicted times between 8 and 12 hours (only 11 faster results/predictions and 15 slower ones are missing).

I have added a “trend line” that shows the best fit of all the data points, highlighting the fact that most of the data points are pretty close to the “diagonal” (where actual = predicted). Between 8 hours and 10 hours the algorithmic predictions are pretty good on average (maybe predictions are a bit too fast around 8 hours). Towards 10 hours finishing time and especially over 10 hours the predictions are too fast: This is caused by “explosions” that lead to very slow times even for athletes that have been predicted to be relatively fast. To put it another way: Finishing times over 10 hours are most often bad races that are pretty much unpredictable using only data.

Here is another way of looking at how far off the time predictions have been from the actual results:

The graph shows the number of results in one minute bins of difference between predicted and actual finishing times. Data points towards the right are faster than predicted, they are slower than predicted to the left. Again a trend line smoothes out the statistical “noise”.

A few observations:

• In a range roughly between -40 minutes and +40 minutes the graph is pretty symmetric and is very close the normal distribution.
• As noted above, there is relatively large number of “explosions” with large negative differences, resulting in a non-symmetrical distribution on the edges of the graph. (There are 49 results that are more than 60 minutes slower than predicted, but only 10 that are more than 60 minutes faster.)

On average, the predictions are -4.7 minutes off the actual finish time (i.e. the actual finish is  slower by close to five minutes). An average close to 0 means that on average the predictions are closer to the actual finish. The standard deviation is 31.8, this means that 68% of the time differences are between -36.5 and 27.1 minutes (-4.7 +/- 31.8 minutes). Usually, a smaller deviation corresponds to a “better” prediction.

The standard “statistical” way of measuring the dependence between two data sets is correlation. Correlation is +1 in the case of a perfect linear relationship, −1 in the case of a perfect inverse relationship, and some value between −1 and 1 in all other cases, indicating the degree of linear dependence between the variables. A value around zero indicates that there is less of a relationship (closer to uncorrelated). The closer the coefficient is to either −1 or 1, the stronger the correlation between the variables. The predictions and actual finishing times have a correlation of 0.77, indicating a pretty strong dependence between the two data sets.

Comparing Other Prediction Strategies

When trying to predict a completely random event (the classical example is a “perfect dice”), the correlation between the actual events and the predictions won’t be very high. When only working off previous results, anything that happens before a race – for example a good block of training, a slight injury – will influence how fast an athlete is able to go on race day, as will random events during the race (e.g. getting punched during the swim, technical issues on the bike). Therefore a “perfect prediction” (resulting in a correlation of 1) is impossible, and in order to determine whether a correlation of 0.77 indicates a “good predictor” or not, one has to compare the results of my algorithm to other predictions.

I am not aware of anyone other than me publishing time predictions for Ironman races on a regular basis. (Please let me know if there is!) Therefore, I am comparing my predictions to a few much simpler strategies:

1. Last Finish: “You are only as good as your last race” (prediction = last IM-distance finish)
2. Best Finish: “My best time is a sub-x” (prediction = fastest IM-distance finish)
3. Average Finish: “I usually finish around y” (prediction = average IM-distance finish)
4. Average Last Year Finish: “This season is going great” (prediction = average IM-distance finish in the last twelve months)

Here’s a comparison of the correlation of these different methods and my comments:

	Number of Data Points	Average Difference	Standard Deviation	Correlation to Actual Finish	Comments
Last Finish	943	-0.75	41.14	0.649	Good on average, but wide deviation and lower correlation
Best Finish	943	-21.48	39.59	0.659	Slightly better deviation and correlation, but large average difference
Average Finish	943	-0.84	35.77	0.706	Good on average, but wider deviation and lower correlation than TTR Predictions (still better than last/best finish)
Average Last Year Finish	877	-1.89	35.08	0.706	Almost the same as the Average Finish, but applicable for fewer cases
TTR Predictions	930	-4.70	31.81	0.770	Lowest deviation, highest correlation

Summary

The Prediction Algorithm I use to calculate the expected times in my pre-race posts provides better predictions than simpler prediction strategies. My model certainly has limitations, but the large number of “successful” winner predictions and the high correlation show that the time predictions and the conclusions drawn from them are pretty much valid. I think my analysis is quite good at telling the “data part of the story”.

While the “data part” is an important (and impartial) part of the story, it is still only a part of the story. A coach or teammate that has been able to observe an athlete getting ready for a race has additional (and more current) information available – even if that is not always fully objective.

The tension between past performances, the uncertainty of a future performance, the challenges athletes face in their training and the hard work they put in to be better in their next race .. that’s why I still love following the races!

With the release of my “TriRating Report 2015” I have also updated my Top 10 Ratings. This post describes the overall ratings, the individual discipline ratings can be found here. The Report also contains a longer discussion of the ratings and 2015 highlights.

Top 10 Women

Rank	Name	Nation	Rating	Last Race	# Races
1	Daniela Ryf	SUI	08:59:29	IM Hawaii on 2015-10-10	5
2	Rachel Joyce	GBR	09:06:50	IM Hawaii on 2015-10-10	17
3	Mirinda Carfrae	AUS	09:08:12	IM Hawaii on 2015-10-10	12
4	Caroline Steffen	SUI	09:11:40	IM Hawaii on 2015-10-10	21
5	Liz Blatchford	AUS	09:15:12	IM Hawaii on 2015-10-10	9
6	Eva Wutti	AUT	09:15:35	IM Hawaii on 2015-10-10	6
7	Susie Cheetham	GBR	09:17:03	IM Hawaii on 2015-10-10	3
8	Julia Gajer	GER	09:17:12	IM Arizona on 2015-11-15	10
9	Yvonne Van Vlerken	NED	09:17:31	IM Western Australia on 2015-12-06	21
10	Mary Beth Ellis	USA	09:17:48	IM Hawaii on 2015-10-10	16

After her dominating 2015 season it’s pretty obvious that Daniela Ryf has claimed the top spot in my rankings. Daniela was unbeaten all year, and won an unprecedented string of races including the European Championships in Frankfurt, the 70.3 World Championships in Zell am See, the Ironman World Championships in Kona and the $1 Million Dollar prize for the Triple Crown
(Photo: Dani on the bike in Kona. Credit: Jay Prasuhn)

Rachel Joyce had another solid season, improving from 3rd to 2nd in my rankings. Once again she was struggling with a mid-season injury but managed to get in excellent Kona shape. She finished second even after loosing time when the zipper of her racing top came undone at the start of the bike and she missed the front group. She’ll be working hard for a shot at winning in Kona next season.

Mirinda Carfrae dropped to third place after a relatively slow (for her standard) validation race in Melbourne and an unfortunate DNF in Kona. I expect her to be back in great shape for 2016 – and hungry to prove that she can still challenge Daniela.

Caroline Steffen (#4) hasn’t had a good 2015 season. She’s had solid results with podium finishes in Melbourne and Frankfurt and a couple of wins in 70.3s, but she was sick too often to train and race well. She is still a good enough athlete to finish 9th in Kona even with food poisoning in the days before. She’ll be looking to step it up again in 2016.

Adopting a similar season plan to her IM debut season in 2013, Liz Blatchford (#5) had much better results than in 2014 and ended her season with a podium finish in Kona. Next year will be interesting for her: Can she solidify her position as one of the best long distance athletes and a Kona podium finisher or will she struggle to confirm the results of her 2013 and 2015 season?

Eva Wutti (#6) had a year with some ups (winning her home IM in Austria with another sub-9 time) and downs (struggling with an injury and having to cancel 70.3 World Champs in her home country). She was disappointed with her 16th place finish in Kona, but I consider that a solid first Kona race considering she was hardly able to run in the weeks before the race.

Susie Cheetham (#7) is the only new athlete in the female Top 10. She was steadily improving in her Ironman races, including a 6th place in Kona. It was only during this season that she became a full-time athlete so she seems to be in a great position to continue improving. With her run strength she’ll be a strong contender in her 2016 races.

Even though she had a disappointing DNF in Kona, Julia Gajer (#8) can be content with her 2015 season. A second place at IM Frankfurt showed that she is able to compete in the strongest Ironman fields. Ultimately her goal is to perform well in Kona, and she’ll be working towards the necessary improvements in the coming months.

Yvonne Van Vlerken (#9) decided not to race in Kona this year and declined her slot. Nonetheless she was racing a lot in the second half of the year, becoming the first athlete to have ten sub-9 finishes and already taking care of Kona qualifying even though she wasn’t in top form. She seems to plan for a much lighter racing schedule in 2016, with a clear focus on Roth and Kona. A healthy and rested Yvonne will be a strong contender for a Kona podium finish.

After some changes in her environment, Mary Beth Ellis (#10) had a couple of good results (wins at the ITU World Championship and at IM Mont Tremblant) but she still had issues to run well towards the end of the marathon that cost her the win at IM Switzerland (she finished 2nd) and a better place than 13th in Kona. In order to place well in Kona, she and her coach Brett Sutton need to figure out how she can run once more under 3:10 as she was able in her best IM years between 2011 and 2013.

Jodie Swallow (now #15) dropped out of the Top 10 – she had a great result in South Africa at the start of the year but was struggling to stay healthy. In Kona she tried to go for the win and had to pay the price with a DNF. Knowing her, she’ll be back in 2016 with aggressive goals.

Top 10 Men

Rank	Name	Nation	Rating	Last Race	# Races
1	Jan Frodeno	GER	08:07:22	IM Hawaii on 2015-10-10	4
2	Sebastian Kienle	GER	08:14:23	IM Hawaii on 2015-10-10	11
3	Brent McMahon	CAN	08:16:31	IM Arizona on 2015-11-15	4
4	Andreas Raelert	GER	08:17:54	IM Hawaii on 2015-10-10	15
5	Frederik Van Lierde	BEL	08:20:27	IM Hawaii on 2015-10-10	17
6	Nils Frommhold	GER	08:20:39	IM Hawaii on 2015-10-10	7
7	Andy Potts	USA	08:22:40	IM Hawaii on 2015-10-10	13
8	Timo Bracht	GER	08:22:49	IM Mallorca on 2015-09-26	25
9	Eneko Llanos	ESP	08:23:08	IM Hawaii on 2015-10-10	23
10	Marino Vanhoenacker	BEL	08:24:38	IM Hawaii on 2015-10-10	20

Similar to last year, there were quite a few changes in the Top 10 Ratings. Jan Frodeno is the clear #1 after a dream 2015 season including wins in Frankfurt, at the 70.3 Championships and in Kona. After he wasn’t ranked last year (he only had two finishes at the end of 2014), the question most often asked is how long he’ll be able to dominate. We haven’t seen a successful male title defense in Kona since 2009 (Craig Alexander) – and Frodo seems to be poised to be the next one.
(Photo: Jan being watched on the run in Kona, Credit: Jay Prasuhn)

Sebastian Kienle dropped back to second place. While he is probably disappointed with his season (especially his race in Kona) and being overshadowed by Frodo, he still had great results and is far from being finished. He’ll be hungry to step up again in 2016.

With Brent McMahon in #3 there is another previously unranked athlete in the Top 10. His 2014 and 2015 results (including a win in Arizona, a 3rd in Brasil and a 9th place in Kona) lead to his designation of “Rookie of the Year”, and he’s probably going to be even stronger in 2016.

Andreas Raelert in #4, Andy Potts in #7 and Marino Vanhoenacker in #10 are athletes that have improved their rating in 2015 and moved back into the Top 10. After a few disappointments in the last years and his DNF in Texas Andreas seemed to be at the end of his career, but he bounced back with a hard earned 6th in Frankfurt and a great second place in Kona, earning “Comeback of the Year”. Andy Potts won his summer IM in Coeur d’Alene and had another good race in Kona finishing fourth, but he’s probably a bit disappointed to once again miss a podium finish by one spot. He’ll turn 40 in 2016, so he won’t have too many season to gain at least one more spot. Marino had two sub-8 finishes when he won IM Brasil and IM Austria with the fastest 2015 Ironman. His only disappointment was a flat day in Kona that lead to a DNF. He’ll try to become the first athlete with IM wins on all continents by winning IM New Zealand in March.

Frederik Van Lierde (#5) and Nils Frommhold (#6) dropped a bit in the rankings. They’ve had a mixed season with some great results (wins in South Africa for Frederik and in Roth for Nils) but disappointing Kona races finishing in 25th and 29th place. I expect them to finish much better in Kona 2016.

Timo Bracht (#8) decided once again to skip Kona. He took some time to recover from his races at the end of 2014, was back in great shape for Roth (but finished second behind a superb Nils Frommhold) and won IM Mallorca at the end of his 2015 season. It seems that he wants to race Kona again in 2016, it’s going to be interesting to see how he’ll be doing (his most recent Kona finish was a 9th place in 2013).

Eneko Llanos (#9) had a solid 2015 season that included a 5th in South Africa, an 8th at IM Germany and a 7th in Kona. He’ll be another well-ranked athlete that is going to turn 40 in 2016, and I’m sure he’ll be looking for at least one more great result.

Last year we’ve also had a couple of other athletes in the top 10:

• Dirk Bockel (was #4) was injured for most of the year, he’ll be looking for a comeback in 2016.
• Craig Alexander (was #6) has continued to race 70.3s in 2015, but he ended his Ironman career after Kona 2014.
• Clemente Alonso-McKernan (was joint #8) had a great end to his 2014 season with three podium finishes in three IMs within eight weeks and needed some extra time to properly recover from those races and also deal with an injury. He DNF’d in Kona and hasn’t finished a 2015 Ironman.
• Bart Aernouts (was joint #8, now #17) had some great 70.3 results including a 4th in the 70.3 World Championships, but DNF’d in Kona.
• Jordan Rapp (was #10, now #15) had ups (a win at IM Mont Tremblant) and downs (DNF in Texas, 21st in Kona after dealing with a broken saddle). Even so he seems to be in a good position to re-enter the Top 10 in 2016.