Is Elite Badminton Getting Older?

A couple of days ago I found a research paper titled Elite Badminton Is Getting Older: Ages of the Top 100 Ranked Badminton Players from 1994 to 2020¹ written by four researchers from Spain, among them Pablo Abian, who has won countless Spanish national championships and international titles.

I already posted an analysis of how old the players are last September. This post will go beyond only counting the ages of players in matches, but assess their strengths and analyse their ages with respect to the world rankings as generated by my simulation. I will then compare these results to the results obtained in the paper.

The paper

The paper was published in November 2021 in the International Journal of Environmental Research and Public Health. Among the four authors is also Pablo Abian’s brother Javier Abian. The abstract says:

The purpose of this study was to determine the evolution of the age of badminton players in the top 100 of the World Ranking for men and women from 1994 to 2020.

The paper is available for free online at ResearchGate.

So we will compare their findings to results obtained using my simulation. We can then ask what the differences between the BWF World Rankings and the rankings according to my simulation are. What systematic differences appear? Are certain players, ages or continents over-represented?

The paper only analyses rankings for men’s and women’s singles. My simulation can provide results for singles as well as for doubles, in this post however we will just analyse the singles categories. The analysis of doubles rankings will be the subject of a future post.

Methodology

Whereas Abian et. al. only used the year-end rankings, I will use rankings obtained at the beginning of each month, as also used in my Monthly World Rankings. Thus we will have twelve times as many rankings per year.

The paper uses data from 1994 onwards. My database only includes matches from 2008 onwards, so we will only generate rankings from 2010 on. The first 16 years from the paper are thus missing from this analysis. We can therefore not draw conclusions on long-term effects. Nevertheless we will compare our results for the time frame since 2010 with the results presented in the paper.

As Age we will use the integer age, that means the age rounded down to a whole number of years. So the age we use will on average be half a year less than the real age when including also the fraction of the year since the last birthday.

Also to clarify the nomenclature as this is a constant source of confusion, we set high to indicate the lowest numerical ranks, i.e. 1 is the highest rank.

Data

Average Age Per Rank - Raw Data

The following plots show the average age for the players placed on the specific rank during all rankings from January 2010 to February 2022.

The plots show the distribution for ranks up to 1000. We can already see that the average age tends to fall when increasing the rank in question. For men’s singles the curve starts around 26 to 28 , then falls to around 22 for ranks around 300 and after that only falls slightly more. For women’s singles we can easily see that the curve is lower, starting at around 24, then falling to around 20. So we can already state that there is a difference of about two years between men’s singles and women’s singles players.

Average Age Per Rank - Top 100

We can see the difference between men and women as well when we take a look at just the top 100. Men’s singles starts higher, which is probably due to the long careers in the top 10 of players like Lin Dan, Lee Chong Wei and Chen Long, who were already established players in 2010 so taht only the later years of their careers are in the data set. Then going to lower ranks the curve fluctuates around an age of about 25 years.

For women’s singles the curve is at most slightly downwards but fluctuates mostly around an age of 23 years.

Average Age Per Rank - Cumulative

We can also take a look at the cumulative average, that means the average of all players and ranks higher or equal to the given rank. Thus we will generate a smoother curve.

Again, we see the higher ages for higher ranks for the men’s singles. As shown in the plot, players within the top 10 are on average 26.8 years in men’s singles and 23.6 years in the women’s singles. When taking all top 100 players into account, the averages decrease to 25.0 and 22.8 years respectively. For lower ranks the difference between the curves remains almost constant at a value of about two years.

Results and Discussion

Average Age Per Year

This plot corresponds to Figure 1A in the paper. It shows the evolution of average age in badminton players in my simulation’s top 100. The dashed horizontal line was added to indicate the average over the complete time frame.

The averages ages of all top 100 players per year is shown in the following table. The row All shows the averages for the whole time frame.

Year	Men’s Singles	Women’s Singles
2010	24.3 ± 3.7	22.7 ± 4.2
2011	24.8 ± 3.9	22.9 ± 4.3
2012	25.1 ± 4.1	23.1 ± 4.2
2013	24.7 ± 4.2	22.8 ± 4.2
2014	24.7 ± 4.4	22.8 ± 4.0
2015	25.1 ± 4.7	22.6 ± 3.9
2016	25.2 ± 4.7	22.5 ± 3.7
2017	25.1 ± 4.6	22.4 ± 3.6
2018	25.2 ± 4.7	22.5 ± 3.3
2019	25.1 ± 4.8	22.8 ± 3.3
2020	25.1 ± 4.7	23.1 ± 3.6
2021	25.1 ± 4.4	23.5 ± 4.1
2022	25.0 ± 4.4	23.1 ± 4.1
All	25.0 ± 4.4	22.8 ± 3.9

Abian et.al. found an increase in the average ages in both the men’s singles and the women’s singles. The averages they found for the year 2020 were 26.3 ± 4.4 years for the men’s singles and 24.7 ± 3.3 years for the women’s singles. The results of my analysis are 25.1 ± 4.7 for men’s singles and 23.1 ± 3.6 for the same year.

So as a first result we can state that the top players in the simulated rankings are about one and a half years younger than their counterparts in the official world rankings. Also we confirmed that top women’s singles players are younger than their male equivalents. We found a slightly larger difference between men and women, 2 years compared to 1.6 years as found in the paper.

When it comes to the development of the average age, there is no clear trend to see in our 13 years of data. For the the men’s singles it seems to increase from 2010 to 2015 and then stay at this higher level, while for the women’s singles the curve seems to follow a gentle wave.

About a possible periodicity in the data, the paper states:

The evolution of age in men and women badminton players from the top 100 results show that the years following Olympic Games (1996, 2000, 2004, 2008, 2012 and 2016) tended to present lower average ages in both modalities. This phenomenon may be due to the symbolic weight of the Olympics for any sports player, who may prolong their career aiming to take part in such a key event. Whether they succeed or not, it is common for players who reached their peak performance several years before to withdraw from elite competition or even retire.

We can as well see indications of this pattern. The Olympic years (2012, 2016, 2021) seem to be connected to slightly larger average ages. Note however that a player who retires after an edition of the Olympic Games will be present in the simulated rankings for at least twelve more months, and thus will still affect that and the following year’s average age. In the world rankings on the other hand he or she might still be in the rankings at the end of the Olympic year, but not anymore the following year. So we expect the influence of Olympic cycles to be more fuzzy in our simulation than in the official rankings.

Also note that our data stretches into 2021 and the beginning of 2022. During these 14 months many tournaments were cancelled. As far as these plots go it seems these cancellations have not altered the average age of top-ranked players.

Average Age on First Entry

In Figure 1B in the paper, the evolution of the average age of badminton players’ on their first entry in the World Ranking top 100 from 1995 to 2020 is shown. The corresponding plot, using my simulated rankings and data from 2011 to February 2022, is shown below.

For our data, we omitted the year 2010 as this was the first year for which the rankings were generated for. It was thus sometimes uncertain if a player had been in the top 100 before. So we only included players who entered the top 100 for the first time since the beginning of the year 2011.

We can see that the average age, a player first enters seems to be increasing during the last years. In 2011 the value was rather high, but this could as well be from older players who were in the top 100 before 2010 and then re-entered in 2011. From 2012 to 2015 the average age is around 18 to 20 for both men’s singles and women’s singles. It then starts to increase towards ages around 21 and 22 in the year 2020 for women and men respectively.

In 2021 and 2022 the ages are even higher, but this could be due to older players gaining entrance into the top 100 due to the low number of tournaments, barring some players from playing enough matches to defend their rankings.

Average Age at Peak

Another question the paper answered was at what age players reach their best ranking. In the paper the resulting plot is shown in Figure 2. The authors write:

The best ranking in their sports career was significantly higher in men compared to women in the top 10, top 25, top 50, top 75 and top 100, and women reached their best ranking at a younger age than men (men = 25.6 ± 3.2 years vs. women = 23.6 ± 3.1 years […])

The corresponding plot using my simulation is given below:

Taking all players who reached the top 100, men reached their best rank at an average age of 24.5 ± 3.5 years, while women reached their best rank at an average age of 22.9 ± 3.7 years. Demanding that the player reached a certain rank doesn’t change the age much, with the exception of the top-ranked. If a player reaches the top rank, it seems that he or she reaches this rank at an earlier age than on average. It could as well be a fluctuation due to the low number of players who reached the #1 spot.

One possible explanation for the difference between the rankings and the simulation could be that the official rankings lag behind by about one year. This is understandable because very good results will lead to easier entry or higher seedings in subsequent tournaments, thus making it easier to gain more ranking points and improve one’s rank further, even though the actual peak of strength has already passed. The simulation meanwhile is ignorant of these factors and thus lags less behind the real development of strengths.

Frequency Distribution from the Top 100

Frequency in relation to top 100 players’ ages during the analysed period (2010–2022) is shown in the following plot. This plot correspond to Figure 3 in the paper.

Comparing this plot we see that the frequency rises earlier than for the world rankings. For example, in the women’s singles about 2% of the rankings are for players aged 16, while in the world rankings this was less than one percent, as can be seen in the paper.

The reason for this different behaviour probably lies in two different aspects. First the lagging behind as explained in the previous section. A young player will have difficulty gaining enough ranking points to enter the top 100 even if he is good enough, because his low ranking decreases his or her chances of entering tournaments that distribute enough ranking points to enter the top 100. The second reason is the lack of ranking points awarded at junior championships. For example, a player who convincingly won the World Junior Championships or the Asian Junior Championships has a realistic chance to be already among the best 100 players in the world. But having played these tournaments will not help him or her in the official world rankings.

In the paper, Table 1 shows the frequency distribution for specific age brackets and the different decades. Due to our limited data, we have to limit ourselves to the 2010s. We can then compare our distribution directly with the results from the paper. The following tables show the comparison.

Men’s Singles

Age Bracket	Abian et. al.	Simulation
14-20 years (%)	10.2	16.0
21-25 years (%)	45.4	42.5
26-30 years (%)	32.5	29.7
>30 years (%)	11.9	11.7

Women’s Singles

Age Bracket	Abian et. al.	Simulation
14-20 years (%)	25.8	31.2
21-25 years (%)	48.7	46.9
26-30 years (%)	20.0	17.9
>30 years (%)	5.5	4.1

We see that in the simulation, there are many more players in the age bracket for the youngest players. For both, men’s singles and women’s singles, the simulation gives about 6% more rankings for players aged 20 and below. As a conssequence of this, the other age brackets then feature lesser entries. This difference can be explained as above.

Top 100 Players per Continent per Year

In Figure 4 in the paper, the authors show the percentages of players in the top 100 from each Continent for both the men’s singles and the women’s singles.

We see that the percentage of Asian players increased from the first half of the 2010s to the second half. The share of European players decreased accordingly. America is represented only in the women’s singles, which is the main difference between the genders. Africa and Oceania are hardly represented in the top 100. For 2021 and 2022 the share of Asian players has dropped significantly. This is due to the lower number of tournaments held in Asia due to the CoVid pandemic. Players from Europe and America increased their share due to the better access to tournaments.

The plots and the tables with the data are shown below.

Men’s Singles

Year	Africa	America	Asia	Europe	Oceania
2010	0.0	0.0	78.3	21.7	0.0
2011	0.0	0.3	76.2	23.6	0.0
2012	0.0	0.2	76.3	23.5	0.0
2013	0.0	0.0	81.4	18.6	0.0
2014	0.0	0.0	83.8	16.3	0.0
2015	0.0	0.0	83.9	16.1	0.0
2016	0.0	0.0	84.6	15.4	0.0
2017	0.0	0.0	83.2	16.8	0.0
2018	0.0	0.0	82.9	16.3	0.8
2019	0.0	0.1	84.6	15.0	0.3
2020	0.0	0.9	82.5	16.6	0.0
2021	0.0	2.6	70.5	26.9	0.0
2022	0.0	3.0	64.5	32.5	0.0

Women’s Singles

Year	Africa	America	Asia	Europe	Oceania
2010	0.0	2.2	81.3	16.3	0.2
2011	0.0	2.2	82.5	14.5	0.8
2012	0.0	2.7	79.1	18.3	0.0
2013	0.0	3.2	81.1	15.8	0.0
2014	0.0	2.3	85.1	12.7	0.0
2015	0.0	4.0	85.2	10.8	0.0
2016	0.0	4.0	84.7	11.3	0.1
2017	0.0	3.1	86.2	10.8	0.0
2018	0.0	2.8	87.9	9.3	0.0
2019	0.0	3.0	87.3	9.7	0.0
2020	0.0	3.3	85.6	11.2	0.0
2021	0.0	4.7	72.6	22.7	0.1
2022	0.0	8.0	70.5	21.0	0.5

Distribution of Length of Stay in the Top 100

This table corresponds to table Table 2 in the paper. It shows the distribution of the number of years remaining in the World Ranking top 100. As we use monthly rankings, we cannot transfer this directly.

We can instead count for all players how many months they spent among the top 100 and distribute them into similar bins as in the paper. The results are in the following table.

Number of months in the Top 100	Men’s Singles ( n = 342)	Women’s Singles ( n = 382 )
36 months or less (%)	55.8	60.5
37 to 72 months (%)	21.1	23.0
73 to 108 months (%)	14.3	10.2
more than 108 months (%)	8.8	6.3

The results are surprisingly similar to the numbers in the paper. The numbers for men’s singles are almost identical. For the women’s singles shorter stays are a bit more frequent in the simulation while in the world rankings women who stayed in the top 100 for 7 to 9 years make up 16.3% of the players. In the simulated rankings, players staying 73 to 108 months in the top 100 only make up 10.2% of the players.

Players with the Most Months among the Top 100

We can also rank players by the number of months they spent in the top 100. The players with the most months are shown in the table below. There are 146 months in the data set, so a player who was in the top 100 during the whole period analysed will have the maximum of 146 months.

Men’s Singles

Three players, Ajay Jayaram from India, Deran Liew from Malaysia and Danish Hans-Kristian Vittinghus, stayed in the top 100 during the full period analysed. All three were born in 1986 and 1987, so during the analysed time frame they were between 23 and 36 years old. Axelsen, born in 1994, only missed the very first month.

Nr.	#Months	Player
1	146	Ajay Jayaram
		Daren Liew
		Hans-Kristian Vittinghus
4	145	Viktor Axelsen
5	143	Tanongsak Saensomboonsuk
6	142	Chen Long
		Jan O Jorgensen
		Lee Dong Keun
9	141	Kashyap Parupalli
10	138	Tien Minh Nguyen
		Tommy Sugiarto
12	135	Lin Dan
		Son Wan Ho
14	134	Brice Leverdez
15	133	Wing Ki Wong
16	132	Sai Praneeth B.
17	130	Chou Tien Chen
18	128	Rajiv Ouseph
19	127	Sony Dwi Kuncoro
20	125	Lee Hyun Il
		Sourabh Verma
		Wei Feng Chong

Women’s Singles

In women’s singles, there are even five players who managed to be in the top 100 during the whole time frame. The years of birth for these players are spread out from 1990 to 1995. They are thus also younger than the men with the longest stays in the top 100.

Additionally it can be seen that women’s careers are usually shorter than careers of men’s singles players as a women’s singles player only needed 114 months to enter this list, whereas a men’s singles player needed 125 months.

Nr.	#Months	Player
1	146	Ratchanok Inthanon
		Saina Nehwal
		Sayaka Takahashi
		Sung Ji Hyun
		Tai Tzu Ying
6	145	Zhang Beiwen
7	142	Nozomi Okuhara
8	140	Sayaka Sato
9	135	Nichaon Jindapol
10	134	Carolina Marin
11	133	Porntip Buranaprasertsuk
		Pusarla Venkata Sindhu
		Yip Pui Yin
14	131	Busanan Ongbumrungpan
15	129	Minatsu Mitani
16	126	Michelle Li
17	121	Li Xuerui
18	116	Sonia Su Ya Cheah
19	115	Ayumi Mine
20	114	Akane Yamaguchi
		Aya Ohori
		Bae Youn Joo

Conclusion

We found that lower-ranked players tend to be younger than the higher-ranked ones, which might be due to younger players trying their luck and then ending their careers when they don’t make it to the top.

We see that in the simulated rankings, players are younger than in the world rankings. All forms of rankings will always lag behind the real strengths as they use past results. We found the explanation that in the world rankings this lag is bigger than in the simulated rankings. One possible remedy to reduce this lag would be to award world ranking points at junior events, so that young players have better chances to rise in the world rankings faster.

We cannot state whether top players are getting older. Our data from 2010 to 2022 is not indicative. In the paper data from 1994 to 2010 was used. Any change in the average age could as well have happened before 2010 and thus not show up in our data. Similar to the findings in the paper we see a slight influence of Olympic years. We also find that with the exception of 2021 and 2022, the percentage of Asian players in the top 100 is even higher in my simulation than in the world rankings. A possible explanation for this could be the easier access to a large number of tournaments for European players. We can confirm the result of the paper that the share of Asian players in the top 100 increased during the 2010s. During the CoVid pandemic the share of Asian players decreased again.

When taking a look at the lengths players stayed in the top 100, the share of players who stayed in the top 100 for more than nine years were 8.8% and 6.3% for men and women respectively. So it was definitely possible to have a world-class career spanning over more than nine years. When searching for the players who stayed in the top 100 the longest, we found several players who had stayed in the top 100 for the entire time frame analysed, for the entire twelve years and two months.

Further discussion about the underlying factors influencing the distribution of ages in world-class badminton is beyond the scope of this post. One relevant question is how changes in prize money can affect the age distribution. Increasing prize money could allow players to afford to have a longer career and be less incentivized to end their career to pursue an employment outside of sports. Another question is if increased knowledge about training and injury prevention prolonged players’ careers and thus led to higher average ages. For more discussion about these questions see the paper and the references therein.

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