Swimming

What If Malcolm Gladwell Scored The ISL?

Courtesy: Steve Gambino

Scoring a swim meet is pretty clear-cut, right? Simply assign points to an athlete’s team based on their finish place in an event and tally up the points after all the events – the team with the highest total points wins. Of course, the specific point award for each place will vary from meet to meet depending on its size, format, and league, but this general method is utilized in hundreds of meets per year at all levels from summer league to high school or college swimming. This same method is even the basis of the scoring system adopted by the sport’s first professional league, the International Swimming League (ISL). It may seem like the only, or at least most obvious, way to do it. After all, framing a sport made up of a series of individual, independent, and mostly asynchronous performances into a team sport requires some sort of aggregation. Assigning points and summing is arguably the simplest, clearest, and most natural method. But, it’s not the only one.

So, how could we do it differently?

Malcolm Gladwell proposed an interesting alternative method on a podcast last July 1. Though the discussion was specifically focused on running and cross country, his suggestion could be generalized to swimming as well: instead of aggregating points (which are assigned somewhat arbitrarily/subjectively anyway), aggregate times.

The motivation here is that a points-based scoring system makes the outcome heavily weighted by the top performers, with those at the bottom of the pack contributing much less significantly, if at all, to the overall team performance. After analyzing the ISL’s base scoring system (2019)² and their amended “Jackpot” System (2020-present)³, it is apparent that the primary driver of what wins the team matchup is simply what team has the most top athletes winning each event individually.

Depending on your perspective and your goals, this may be good. For the ISL specifically, which may wish to market itself off the star power of the top athletes, this may be ideal for growing its brand. But as Gladwell notes, “Right now, if I’m the 20th best runner at my high school, what’s my motivation for joining the cross-country team?”¹ So, for some (perhaps high school swimming or similar), alternatives may be worth considering.

Of course, these changes don’t come without tradeoffs too. While, as Gladwell states, “your 20th runner matters as much as your first runner,”¹ under this system, it also means your 20th runner (or swimmer) has a lot more pressure to perform well if the team is relying upon them to win. This may or may not be beneficial depending on your perspective.

As always, determining what scoring system is best comes down to a value assessment. Each sport, level, and league will all have different values and different goals, and thus need to determine what system works best for them. But the ISL is a professional league with elite athletes, and more importantly for now, with publicly available and easily accessible results that can be analyzed and experimented with. So…

What if Malcolm Gladwell scored the ISL?

Season 3 Results

The table below details the results of each match from the ISL’s third and most recent season (2021), including the teams’ original point totals, their aggregate time (Gladwell), and their ranking based on the time (G Rank). For each match, the teams are listed in order of their original ranking. Increased finish places are highlighted in green and decreased finish places are highlighted in red/orange.

Note: The aggregate times are listed in seconds throughout the article for sake of simplifying the math. If this system were to be implemented, it would likely be best to display them in an H:MM:SS format. For example, Energy Standard’s Match 1 time of 8344.51 seconds would be displayed in competition/results as 2:19:04.51 for 2 hours, 19 minutes, and 4.51 seconds.

Table 1: Season 3 Results by ISL (Original) and Gladwell Scoring (in seconds)

Regular Season

MATCH 1
Original
Gladwell
G Rank

MATCH 7
Original
Gladwell
G Rank

Energy Standard
511.5
8344.51
2

Cali Condors
581.0
8336.65
2

Toronto Titans
496.5
8325.54
1

Toronto Titans
529.5
8309.24
1

Aqua Centurions
442.5
8363.40
3

Iron
362.5
8402.19
3

DC Trident
311.5
8364.88
4

NY Breakers
293.0
8405.63
4

MATCH 2
Original
Gladwell
G Rank

MATCH 8
Original
Gladwell
G Rank

Cali Condors
707.0
8386.52
1

Energy Standard
508.0
8316.32
1

LA Current
402.5
8403.93
2

London Roar
490.5
8321.16
2

Tokyo Frog Kings
382.5
8412.39
3

LA Current
386.5
8372.08
4

NY Breakers
269.0
8457.26
4

Tokyo Frog Kings
381.0
8328.51
3

MATCH 3
Original
Gladwell
G Rank

MATCH 9
Original
Gladwell
G Rank

Energy Standard
640.5
8334.47
1

Energy Standard
568.0
8332.68
1

London Roar
436.5
8373.50
2

London Roar
457.5
8375.62
4

Iron
405.5
8393.78
3

Toronto Titans
380.5
8359.54
3

NY Breakers
278.5
8428.08
4

DC Trident
357.0
8359.53
2

MATCH 4
Original
Gladwell
G Rank

MATCH 10
Original
Gladwell
G Rank

Cali Condors
594.0
8311.60
2

Aqua Centurions
563.0
8324.27
2

LA Current
444.5
8295.74
1

Iron
529.0
8326.94
3

Aqua Centurions
375.5
8340.68
4

Tokyo Frog Kings
397.0
8312.98
1

DC Trident
359.0
8314.46
3

NY Breakers
272.0
8387.65
4

MATCH 5
Original
Gladwell
G Rank

MATCH 11
Original
Gladwell
G Rank

Toronto Titans
533.0
8277.58
1

DC Trident
506.0
8287.17
1

LA Current
453.5
8299.27
3

Iron
497.0
8347.41
4

DC Trident
418.5
8289.86
2

NY Breakers
388.5
8340.04
2

Iron
374.0
8378.70
4

Tokyo Frog Kings
385.5
8344.40
3

MATCH 6
Original
Gladwell
G Rank

London Roar
529.5
8325.63
3

Cali Condors
478.5
8322.03
2

Aqua Centurions
379.5
8344.61
4

Tokyo Frog Kings
376.5
8316.56
1

Playoffs

MATCH 12
Original
Gladwell
G Rank

MATCH 15
Original
Gladwell
G Rank

Cali Condors
534.5
8345.67
2

London Roar
534.5
8304.34
2

Energy Standard
522.0
8329.85
1

Cali Condors
474.5
8300.01
1

DC Trident
359.5
8385.69
3

LA Current
438.5
8349.40
3

Iron
340.0
8446.66
4

Iron
324.5
8388.27
4

MATCH 13
Original
Gladwell
G Rank

MATCH 16
Original
Gladwell
G Rank

LA Current
506.0
8353.69
1

Energy Standard
539.5
8322.63
2

London Roar
494.5
8362.21
2

London Roar
498.5
8313.07
1

Toronto Titans
398.5
8363.09
3

LA Current
394.5
8382.16
3

Aqua Centurions
357.0
8470.55
4

DC Trident
284.5
8385.58
4

MATCH 14
Original
Gladwell
G Rank

MATCH 17
Original
Gladwell
G Rank

Energy Standard
583.0
8298.11
1

Cali Condors
532.5
8320.49
1

Toronto Titans
407.0
8320.52
2

Iron
446.0
8394.54
3

Aqua Centurions
390.0
8360.66
4

Aqua Centurions
444.5
8409.00
4

DC Trident
383.0
8320.61
3

Toronto Titans
337.0
8353.93
2

Championship

MATCH 18
Original
Gladwell
G Rank

Energy Standard
534.0
8249.02
1

Cali Condors
522.0
8267.77
2

London Roar
393.5
8307.27
3

LA Current
305.5
8396.04
4

The first thing to note is that 14 out of the 18 matches would be impacted by Gladwell’s scoring system. This may be expected or even seem obvious; but you may recall that the ISL’s Jackpot System (the additional tweak to the standard system that was introduced in Season 2) had nearly no impact on the final results of the meets⁴. So, the fact that Gladwell’s scoring system altered the majority of matches in Season 3 is, in fact, significant.

The teams’ final scores are also much closer to one another under Gladwell’s system than the original. Throughout all of Season 3, the original scores ranged from a low of 279 points (NY Breakers in Match 2) to a maximum of 707 points (Cali Condors in Match 2) – a range of 428 points. Gladwell’s scores, on the other hand, ranged from a “minimum” of 8470.55 seconds (Aqua Centurions in Match 13) to a “maximum” of 8249.02 seconds (Energy Standard in Match 18) – a range of only 221.53 seconds (less than 4 minutes). Despite scores on the magnitude of 8000s, the range in Gladwell’s scoring system is actually smaller than the range of points from the original system. That means that these matches would be significantly closer under an aggregate time system. The chart below compares the range of scores of each system across every meet from Season 3.

The 1st place and 4th place teams differ by an average of about 223 points, but only a difference of about 77 seconds. This trend becomes even more apparent upon a closer look at an individual match. Let’s consider the Season 3 Championship (Match 18). This is one of the few matches where the final team ranking is identical between systems; however, their story throughout the match is very different.

The chart above shows the accumulated score of each team in both systems after each event of the match. The ISL’s original scores are colored in orange. Notice that as we scan left to right (i.e., as the match progresses) the scores of each of the four clubs spread out into four (mostly) distinct and clearly separated lines. Energy Standard and Cali Condors remain somewhat close, but LA Current and London Roar fall very distinctly below them as early as event 10, and continue to remain far behind throughout the remainder of the match. In contrast, using Gladwell’s scores (colored in dark blue), the four teams are barely distinguishable. This means that where the original system creates a large separation of teams, clearly distinguishing the final results relatively early in the match, Gladwell’s system makes the match quantitatively closer all the way until the very end. Under the original scoring system, the LA Current were basically out of contention for a win by the end of event 20. More specifically, the Cali Condors had scored 309 points by event 25 – more than the LA Current’s final total of 305.5. Instead, using aggregate time, the Current don’t exceed the leading time of 8249.02 until event 38 (the second to last event). So, it appears each team can contend down to the wire.

This fundamentally changes the nature of the meet and, in theory, could add substantially more drama!  Every race and every individual swim matters. Does Katie Ledecky win by a pool length in the 400 free? Top swimmers hitting jaw-dropping times can make a huge difference toward keeping the total down. But also, does Sara Franceschi drop a personal best in her 200 IM to bring LA Current a few seconds closer to the mix? As the 8th place finisher in the 200 IM in Match 18, Sara touched the wall more than 3 seconds behind the 7th place performer. Sara’s 1-point contribution from this race was basically a given, but also essentially trivial toward the overall team score. But now, the performances of competitors like Sara can be greatly impactful too. As Gladwell puts it, “under this system – the 20th person – we are as passionately interested in how well they run, as we are in the first.”¹

Prioritizing Time

Time is emphasized as a significant component under Gladwell’s system as well. The ISL has tried to avoid this for much of its tenure, often not even displaying finish times during the broadcasts. Instead, they’ve opted to prioritize the “who-beats-who” matchup nature of races. Whether this is for better or worse depends on your perspective, but there is no denying that time is a fundamental aspect (or the fundamental metric) of the sport at every elite (and sub-elite) competition. For swimmers and swim fans who are intimately familiar with times, I would be curious to see if they find Gladwell’s system more engaging as well.

The flip side of this is that it devalues races where the winner out-touches their competitor by only a few hundredths. If Katie Ledecky wins by a pool length, her contribution toward her team is substantial under Gladwell’s system. However, when Tom Shields won against Caeleb Dressel by two-tenths in the men’s 100 fly from the Season 3 championship, it was an exciting upset (and 6-point swing) in the LA Current’s favor; but this becomes much more trivial (and possibly less exciting) when aggregating times. Now, a single 6-point swing wasn’t exactly going to change the overall outcome of the match either, but using Gladwell’s system instead could certainly have shifted the excitement away from a close head-to-head matchup like this. Whether or not this tradeoff is worth it, depends on the league’s perspective and priorities.

Predictability – Does Quantitative Closeness Correspond with Competitive Closeness?

While the relative closeness of the teams under Gladwell’s system may make the meets seem more dramatic in theory, it may not actually play out that way in practice. The smaller quantitative and proportional difference between teams does theoretically provide an opportunity for a trailing team to have a big swim that allows them to overtake a competitor. Likewise, one DQ, mistake, or bad performance from a leading team could cost them that lead. However, big swings may not be particularly likely in many races, especially the shorter sprint-focused events. In general, there isn’t necessarily a high degree of variance in times between athletes’ performances. So, opportunities to exchange leads may not be much greater under this system. In other words, swimmers tend to perform relatively consistently, which means overall, the outcomes might be relatively predictable under this system. Does the proportional “closeness” of the scores actually correspond with a “competitive closeness?”

This can be true under either system though. I’m certainly not the first to make the suggestion that swimming’s difficulty in becoming an exciting and dramatic spectator, team sport is in large part a function of its overall predictability. To say whether one scoring system is more or less predictable than the other though, would require a more extensive analysis on the predictability of swimming more generally. For now, though, we’ll leave this as a hypothesis.

At least briefly, we can observe the relative similarity between the four teams’ accumulation of time over the last few events of the Season 3 championship match. In the graph below, the lines representing each team change at nearly the same rate from one event to the next. There is a little variation, but for the most part, the slopes of the lines in the graph below appear nearly identical. Also, their relative order (i.e., the ranking of the teams) doesn’t change.

This would allude to a high degree of predictability and a low degree of competitive closeness. This is not universally true across all meets, however, which can be seen, for example, from Match 4. Recall that this was a match where Gladwell’s system would change the final team ranking. Examining the chart below, we can see, this match is notably closer and does include a few ranking changes during the final few events.

If we scale these scores out of the total time accumulated from all four teams after each event, then replot each team’s proportion (i.e., some math to make the graph clearer), we can see the ranking changes further emphasized in the plot below.

Additionally, we can contrast this with the same chart, but plotted with the point-based system instead. Here we can notice fewer lead changes and a much larger general spread (note the different scale of the vertical axis). This particular instance provides some evidence and support for the hypothesis that Gladwell’s system appears to better favor “competitive closeness.”

Thus, it seems that under Gladwell’s system, some meets will feature a “competitive closeness” (and some more so than the original system), though not necessarily all. Again, to what extent this is relevant or impactful requires a broader and more thorough analysis – one to save for another time.

Proportionality 

One more thing we can consider, though, is how much this changes the value per event. Previously, sprinters and freestylers were theoretically the most highly valued swimmers simply because those events represented the largest proportion of points.²

Gladwell’s system works differently. Since distance events are longer in duration, they contribute a larger proportion of the overall score relative to the traditional system. The exact proportions will vary from match to match, as the times swum will vary from match to match, but for sake of comparison, we can examine the breakdown for Season 3’s championship match in the table below.

Table 2: Percentage of Total Score from Match 18 by Distance

Distance
Points
Time

50
26
8

100
41
39

200
21
29

400
12
24

Notes:

All values are rounded to nearest whole percentage.
Since penalty points can be removed, the percentages for points may vary slightly from meet to meet.
Relays (100s) and Skins(50s) are included.

Table 3: Percentage of Total Score from Match 18 by Stroke

Stroke
Points
Time

Back
17
11

Breast
12
11

Fly
17
11

Free
26
30

Medley
27
37

Gladwell’s system increases the value of distances races and reduces the value of sprint races. It also benefits the medleys (individual and relay counted together), and the freestyle events, while reducing the relative value of butterfly, backstroke, and breaststroke. The bump in value of medley races comes from the increased significance of the 400 IM since it is of longer duration. The drop in value of fly, back, and breast, come mostly from significantly reducing the value of the skins event. These were previously worth more points than regular events, but since they are 50s, they’re of relatively shorter duration. Specifically, note that Match 18 featured Skins of women’s backstroke and men’s butterfly (this is why those strokes account for 17% of the points compared to breaststroke’s 12%).

The Tokyo Frog Kings perfectly emphasize this shift in value toward the distance races since their relative success in distance events compared to sprints propelled them up in rank under Gladwell’s system in 4 out of 5 of their matches. The most dramatic change was Match 6, where they jumped up from 4th with the original scoring to 1st with Gladwell’s. The plot below visualizes Tokyo overtaking the lead at Event 17, the women’s 400 free, where Paige Madden (1st) and Chihiro Igarashi (4th) dominated the athletes from Cali Condors and London Roar. (Aqua Centurions had some good performances too, but they were otherwise too far out of the mix for it to matter here).

This is further emphasized with a quantitative comparison. In the final results, Tokyo beat Cali and London by 5.47 and 9.07 seconds respectively. However, the accumulation of time across the 400-meter races alone is more than enough to make up that difference.

Table 4: Match 6 Accumulated Time Across 400m Races

Team
Accumulated Time Across all 400’s
Time Relative to Tokyo (seconds)

Aqua Centurions
1975.53
-6.56

Tokyo Frog Kings
1982.09
0

Cali Condors
2005.01
+22.92

London Roar
2018.6
+36.51

Conclusion

So, what would happen if Malcolm Gladwell scored the ISL? The matches could be closer, though quantitative or proportional closeness may or may not contribute to a more exciting match to watch. The contributions of all athletes (the top stars and the “depth” alike) would appear to become more impactful toward the team outcome. The concept of time would be reinstated as the focal point of the sport rather than just head-to-head matchups. Distance events would have a much heavier influence on the final outcomes. And, in theory, pending further analysis of the predictability of the sport more generally (and assuming the ISL could properly tell the story and display the score intuitively, accurately, and clearly – admittedly not an automatic assumption), each meet could be more dramatic to watch throughout because every swim and every second dropped or added makes a more notable contribution to the final outcome.

Should the ISL actually instate this system? This is hard to say. Scoring is not actually very clear cut and the variety of ways to score the sport of swimming each come with different benefits and tradeoffs. Only the ISL can decide for themselves whether Gladwell’s system’s particular values align with their own. Personally, I doubt they would implement a system like this. However, it was fun to consider and explore, and I would definitely be curious to tune in if they did.

Appendix

What about DNS, DQ’s, and Skins?

Gladwell’s initial proposition of this system was intended for cross country, but swimming has a few more quirks to consider. Three main issues that need to be addressed are DNS’s / No-Swims, DQ’s, and Skins. To summarize each:

DNS (Did Not Swim): These cause an issue because any time an athlete doesn’t swim, they don’t contribute to the total. If unaddressed, this would give an advantage to their team. If this system was to be implemented, you could theoretically also introduce a “No-DNS / No-Scratch” policy. However, this could bring on another set of potential issues and isn’t possible to consider retroactively.
DQ’s: Although most of the DQ’d athletes end up with a finish time in the results, it feels unfair to use that time. DQ’s would typically result in no points or a point penalty, not a potentially positive contribution to the score. So, some way to penalize a DQ would need to be determined.
Skins: The athletes who win the first and second rounds will earn additional swims, contributing more time to their total, while the losing athletes, wouldn’t add that additional time. This works in opposition to what we’d need – penalizing athletes for advancing to subsequent rounds, rather than rewarding them.

A potential solution to address all three of the issues above simultaneously is to use a time cap. For each event, we can assign a “Cap Time” to any DNS, DQ, or subsequently missed Skins race that will add more time to their total than had they swam the race normally and legally. In particular, the ISL already has a list of penalty times⁵. These were originally designed to incur point penalties for swimmers who swim too slowly, but can serve as the Cap Time here instead. For example, the Cap Time for the men’s 50m butterfly is 24.00; so, if an athlete swims a 23.00, but gets disqualified, their time will be replaced with a 24.00. This method was applied to the scoring of Season 3 for this analysis.

It would also be an option to apply the Cap Time as a default for any swimmer who performs slower than this cut-off as well, creating an artificial upper bound for each event’s score. In other words, if an athlete in the men’s 50 butterfly swam a 24.50, for instance, their score would be replaced with 24.00. However, this may cause an undesired consequence: teams could strategically and purposefully enter athletes who won’t swim faster than the Cap Time in order to potentially gain benefits in other races. I did not default any slower times to the Cap Time in my calculation of the Season 3 results because of this.

As a final note on Cap Times, the specific values would have the potential to be particularly impactful on the outcomes of matches. If the league was to implement this system, the choice of Cap Times should be considered carefully. This is especially true with the Skins races, since those performances tend to get progressively slower each round. Thus, it is probably best to assign separate Cap Times for each round of the Skins events to adequately account for this.

The Data + Author’s Note

The Data for this project was sourced from Omega Timing.⁶ They store the results for each of these meets in Lenex format (a swim-specific XML variant).⁷ I wrote a python script to convert these into CSV files to use for my analysis. However, it should be noted that there were a few data issues. In particular, relay entries were often problematic. For the most part, these entries are stored under a name, formatted like “Energy Standard” or “Energy Standard 2,” rather than a unique id. This is still relatively easy to identify when stored in this format. However, some were not stored this way. This created a few duplicated entries when running my code for certain relays in meets 3, 4, 6, and 7. In Match 3 specifically, this caused an addition of about 1000 seconds to the total of Cali’s time from multiple duplicated relay entries. I’ve corrected for these manually (and the results as corrected are what is displayed above), but I thought it should be noted here in case there were further errors in the stored results that I did not catch. I did run some checks to catch other errors, and although these results are almost certainly 99% accurate, please don’t take these specific numbers as gospel. For a “for-fun” project like this, I was aiming for “good enough” over perfection.

Finally, it is important to note that Gladwell himself did not advocate for this system for the sport of swimming, nor elite sports in general. The referenced discussion was in the context of cross country, and more specifically high school (or similarly amateur, sub-elite) level, aggregated team performances. Experimenting with extrapolating that to elite swimming was my “for-fun” swim-data project for my winter break – I have not reached out to him for comment on any of this and would encourage anyone who is curious to listen to the full podcast for context (link in the references below).

References

Roll, R., & Gladwell, M. (2022, July 18). Malcolm Gladwell is lord of all things overlooked and misunderstood. Rich Roll. Retrieved December 30, 2022, from https://www.richroll.com/podcast/malcolm-gladwell-692/
Gambino, S. (2019, October 16). Where swimming & strategy meet: An analysis of the ISL’s scoring system. SwimSwam. Retrieved December 30, 2022, from https://swimswam.com/where-swimming-strategy-meet-an-analysis-of-the-isls-scoring-system/
Gambino, S. (2022, January 10). Revisiting The ISL Scoring System: An Analysis of Jackpot Points (Part 1). SwimSwam. https://swimswam.com/revisiting-the-isl-scoring-system-an-analysis-of-jackpot-points-part-1/
Revzin, B. (2021, September 14). Jackpots: Do They Matter? SwimSwam. https://swimswam.com/jackpots-do-they-matter/
Anderson, J. (2020, October 17). Full Scoring Format For the 2020 ISL Season. SwimSwam. https://swimswam.com/full-scoring-format-for-the-2020-isl-season/
All Results Of The Competitions Officially Timed By OMEGA. (2021). Omega Timing. https://www.omegatiming.com/sports-timing-live-results/2021
Kaufmann, C. (2021, November 2). Lenex 3.0 – Technical documentation. swimrankings.net. https://wiki.swimrankings.net/images/6/62/Lenex_3.0_Technical_Documentation.pdf

ABOUT STEVE GAMBINO

Steve grew up swimming in Middletown, CT. He’s an age group coach with Crimson Aquatics where he’s coached since 2016 – first in Rhode Island, now in Wellesley, MA. Steve has an M.S. in Mathematics from University of Rhode Island, has previously served as a consultant for the ISL for the development of their rating system, and currently lives in Worcester, MA, where he works as an Assistant Professor of Mathematics at Quinsigamond Community College.

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