4  Competition Metrics

4.1 How common is competing in competitions for professional dancers in the US?

Note

We currently only have this data for the 2020-2021 Season for 25 companies.

4.2 Who competes in Prix de Lausanne and where do they train?

4.2.1 In Progress

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Note

The 2025 edition of Prix de Lausanne begins Feb 2nd to Feb 9th. Details about the selected candidates can be found here.

Note

We currently only have complete data for 2020 to 2024. Some data from 2018 to 2020 is available and annotations for time periods are included in the analysis. This information is taken from public releases available online from Prix de Lausanne.

Data Pointes is not sponsored by or affiliated with Prix de Lausanne.

The Prix de Lausanne is an annual international ballet competition for young dancers aged 15 to 18. Held in Lausanne, Switzerland, it spans a week and attracts participants from around the world. The competition begins with video applications or preselection at special events, from which select dancers are invited to Lausanne. During the week, dancers take daily ballet and contemporary classes, receive individual coaching, and perform before a jury. The event culminates in a final round of roughly 20 dancers where selected participants perform classical and contemporary variations.

Beyond competition, the Prix de Lausanne serves as a networking platform, connecting young dancers with directors of prestigious ballet schools and companies. Awards include scholarships to renowned dance schools and contracts with major ballet companies. The competition’s format is designed not just to identify winners, but to provide all participants with professional feedback and potential career opportunities, making it a significant event in the ballet world for launching young dancers’ careers. Thanks to excellent social media coverage of the annual competition, participating in the Prix de Lausanne finals provides a global platform for young dancers to be seen. A comprehensive description of the selection process and format can be found here.

Note

Note: Complete public data including submissions, preselected candidates, competitors, and finalists only extends back to 2020 although some of this data from earlier competitions is available.

2021 was a special video edition of the competition held virtually for the COVID-19 pandemic.

The Prix de Lausanne organization lists Hong Kong in a few different ways (typically as Hong Kong, China). We decided to use ‘Hong Kong, China’ as it was the most recent version listed. It is unclear if some competitors with nationalities listed as China are from Hong Kong (likely not).

4.2.2 How competitive is Prix de Lausanne?

There have been 2003 total submissions to Prix de Lausanne since 2020. Of these, 578 have qualified for the competition (28.9%). 146 have advanced to the finals (7.3%). Finally, 49 have won a prize (2.4%).

4.2.2.1 Numbers of Submitters, Competitors, and Finalists From 2020 to 2024

These numbers vary by year. For reference, 2021 was a special video edition due to lockdowns during the COVID-19 pandemic. In general, the competition has become more competitive based on number of submissions to finalist spots.

Year	# of Submissions	# of Qualifiers	# of Finalists
2020	377	84	21
2021	399	81	20
2022	376	81	20
2023	426	86	23
2024	425	88	20

4.2.2.2 Number of Submitters by Gender From 2020 to 2024

As Prix de Lausanne separates competitors into categories by “BOYS” and “GIRLS”, we use this terminology for the analysis. For how we analyze gender elsewhere in the Data Pointes guide, see our section on how we record gender. Data on submissions only includes gender category and is only public from 2020 onwards:

Year	# of BOYS Submissions	# of GIRLS Submissions
2020	76	301
2021	81	318
2022	78	298
2023	94	335
2024	92	333

Consistently, there is roughly 3.7 times as many GIRLS submissions than for BOYS each year. The number of submissions has increased for both categories over time.

4.2.2.3 Number of Competitors and Finalists by Gender Over Time

Competitors are also divided into age groups. ‘Group A’ roughly corresponds to ages 14 to 16 years old during the competition while ‘Group B’ is 17 to 18 years old. Age is determined by date of birth, accounting for countries which used different age systems in the past such as South Korea and China. The BOYS A category consistently has the least number of competitors. BOYS B has overtaken GIRLS A as the largest category starting from 2023. Trends over time are less clear for categories of dancers who make it to the finals.

Competitors

viewof compQYG = Inputs.checkbox(
  ["BOYS A", "BOYS B", "GIRLS A", "GIRLS B"], 
  { value: ["BOYS A", "BOYS B", "GIRLS A", "GIRLS B"], 
    label: "Groups:"
  }
)

filteredDataQYG = transpose(dataQYG).filter(function(dancer) {
  return compQYG.includes(dancer.Group);
})

Plot.plot({
y: {
    grid: true,
    domain: [0, 40]
  },
  marks: [
    Plot.line(filteredDataQYG, {
      x: "Year",
      y: "# of Competitors",
      stroke: "Group",
      strokeWidth: 2,
      curve: "linear"
    }),
    Plot.dot(filteredDataQYG, {
      x: "Year",      // feature for the x channel
      y: "# of Competitors",     // feature for the y channel
      fill: "Group",  // feature for the stroke
    }), 
  ],
  width: 554, 
  height: 280, 
  x: { ticks: 3 }, 
  marginLeft: 50, 
  color: {
    legend: true,
    width: 554,
    columns: "120px",
    domain: ["BOYS A", "BOYS B", "GIRLS A", "GIRLS B"],
    range: ["#89bfa7", "#e2936b", "#95a0c7", "#d290c0"]
  }
})

Finalists

viewof compFYG = Inputs.checkbox(
  ["BOYS A", "BOYS B", "GIRLS A", "GIRLS B"], 
  { value: ["BOYS A", "BOYS B", "GIRLS A", "GIRLS B"], 
    label: "Groups:"
  }
)

filteredDataFYG = transpose(dataFYG).filter(function(dancer) {
  return compFYG.includes(dancer.Group);
})

Plot.plot({
y: {
    grid: true,
    domain: [0, 15]
  },
  marks: [
    Plot.line(filteredDataFYG, {
      x: "Year",
      y: "# of Finalists",
      stroke: "Group",
      strokeWidth: 2,
      curve: "linear"
    }),
    Plot.dot(filteredDataFYG, {
      x: "Year",      // feature for the x channel
      y: "# of Finalists",     // feature for the y channel
      fill: "Group",  // feature for the stroke
    }), 
  ],
  width: 554, 
  height: 280, 
  x: { ticks: 3 }, 
  marginLeft: 50, 
  color: {
    legend: true,
    width: 554,
    columns: "120px",
    domain: ["BOYS A", "BOYS B", "GIRLS A", "GIRLS B"],
    range: ["#89bfa7", "#e2936b", "#95a0c7", "#d290c0"]
  }
})

4.2.2.4 Competitiveness By Gender

By averaging the numbers of submitters, competitors, finalists, and prizewinners for each competitor gender category, we can see relative competitiveness.

Categories	% Competitors / Submitters	% Finalists / Submitters	% Prizewinners / Submitters
BOYS	41.4	13.1	5.7
GIRLS	11	3.5	1.5

Across all stages, a dancer who submits their application in the BOYS category has a roughly 3.7 times higher probability of making it to the next stage (competitor to finalist to prizewinner) than a competitor in the GIRLS category. Based purely on the number of submitters, Prix de Lausanne is more competitive for the GIRLS category, not accounting for other factors that influence competitiveness such as the skill level of the applicant pool.

Looking at acceptance rates is only one part of evaluating competitiveness by gender. Considering the relative skill levels of each group and understanding why/when different groups choose to compete provides more insight.

4.2.3 Where are most competitors from?

As an international competition, submissions to compete in the Prix de Lausanne come from all over the world. Since 2020, submissions have been sent from 69 different countries. Let’s see how these breakdown in terms of where submissions come from (submitters), who gets selected to compete in person (competitors), and who makes it to the final round (finalists).

4.2.3.1 Nationalities of Submitters, Competitors, and Finalists Overall

Since 2020, Japan, the United States, and Australia had the top 3 highest amount of submissions. However, South Korea had the most participants make it to the competition despite being the 4th highest in number of submissions. South Korean submissions had a higher chance of becoming competitors than all other countries with large numbers of submissions (more than 50 submissions)–a success rate of 38.7%. Similarly, China had the next highest success rate for a high submission country with a 35% success rate. These statistics are in line with the high participation rate of Asian-born dancers in the competition and final round. Despite this, Brazil is the top “high submission” country for getting submitters to the final round, followed by South Korea. To gauge how Prix de Lausanne participation by country compares to representation in major companies, see our previous data on nationalities in top US companies here.

4.2.3.2 Nationalities of Submitters, Competitors, Finalists, and Prizewinners By Year

Submitters

tdataSY = transpose(dataSY)

// Year selector
viewof selectedYear = Inputs.select(
  ["2020", "2021", "2022", "2023", "2024"],
  {value: "2024", label: "Select Year"}
)

Plot.plot({
  width: 800,
  height: 500,
  marginBottom: 90,
  marginLeft: 50,
  x: {
    label: "Nationality",
    tickRotate: -45
  },
  y: {
    grid: true,
    tickFormat: "d",
    label: "Count"
  },
  color: {
    legend: false
  },
  marks: [
    Plot.barY(tdataSY.filter(d => d[selectedYear] !== "NA" && d[selectedYear] !== undefined), {
      x: "Nationality",
      y: d => +d[selectedYear],
      fill: "Nationality",
      title: d => `${d.Nationality}: ${d[selectedYear]}`,
      sort: {x: "y", reverse: true}
    }),
    Plot.ruleY([0])
  ]
})

* This data is only available from 2020 to 2024.

Competitors

tdataCY = transpose(dataCY)

// Year selector
viewof selectedYearCY = Inputs.select(
  ["2018", "2019", "2020", "2021", "2022", "2023", "2024"],
  {value: "2024", label: "Select Year"}
)

Plot.plot({
  width: 800,
  height: 500,
  marginBottom: 90,
  marginLeft: 50,
  x: {
    label: "Nationality",
    tickRotate: -45
  },
  y: {
    grid: true,
    tickFormat: "d",
    label: "Count"
  },
  color: {
    legend: false
  },
  marks: [
    Plot.barY(tdataCY.filter(d => d[selectedYearCY] !== "NA" && d[selectedYearCY] !== undefined), {
      x: "Nationality",
      y: d => +d[selectedYearCY],
      fill: "Nationality",
      title: d => `${d.Nationality}: ${d[selectedYearCY]}`,
      sort: {x: "y", reverse: true}
    }),
    Plot.ruleY([0])
  ]
})

Finalists

tdataFY = transpose(dataFY)

// Year selector
viewof selectedYearFY = Inputs.select(
  ["2018", "2019", "2020", "2021", "2022", "2023", "2024"],
  {value: "2024", label: "Select Year"}
)

Plot.plot({
  width: 800,
  height: 500,
  marginBottom: 90,
  marginLeft: 50,
  x: {
    label: "Nationality",
    tickRotate: -45
  },
  y: {
    grid: true,
    tickFormat: "d",
    label: "Count"
  },
  color: {
    legend: false
  },
  marks: [
    Plot.barY(tdataFY.filter(d => d[selectedYearFY] !== "NA" && d[selectedYearFY] !== undefined), {
      x: "Nationality",
      y: d => +d[selectedYearFY],
      fill: "Nationality",
      title: d => `${d.Nationality}: ${d[selectedYearFY]}`,
      sort: {x: "y", reverse: true}
    }),
    Plot.ruleY([0])
  ]
})

Prizewinners

tdataPY = transpose(dataPY)

// Year selector
viewof selectedYearPY = Inputs.select(
  ["2020", "2021", "2022", "2023", "2024"],
  {value: "2024", label: "Select Year"}
)

Plot.plot({
  width: 800,
  height: 500,
  marginBottom: 90,
  marginLeft: 50,
  x: {
    label: "Nationality",
    tickRotate: -45
  },
  y: {
    grid: true,
    tickFormat: "d",
    label: "Count",
    domain: [0, 5],  // Start from 0
    nice: true,         // Round to nice values
    ticks: 5            // Specify number of ticks
  },
  color: {
    legend: false
  },
  marks: [
    Plot.barY(tdataPY.filter(d => d[selectedYearPY] !== "NA" && d[selectedYearPY] !== undefined), {
      x: "Nationality",
      y: d => +d[selectedYearPY],
      fill: "Nationality",
      title: d => `${d.Nationality}: ${d[selectedYearPY]}`,
      sort: {x: "y", reverse: true}
    }),
    Plot.ruleY([0])
  ]
})

* Competitors who win more than one prize are only counted once. This data is only available from 2020 to 2024.

4.2.3.3 Nationalities of Submitters, Competitors, Finalists, and Prizewinners Over Time

The following interactive plot demonstrates how the number of dancers from each country has changed over time:

Danger

Lines can cover each over. If you do not not see a line initially, try de-selecting other countries to see if it is being covered. If there is still no line, that country did not have any dancers in that category for that year.

Submitters

viewof compNS = Inputs.checkbox(
  ["Argentina", "Australia", "Belarus",   "Belgium",   "Brazil",  "Bulgaria",  "Canada",   
"China",   "Cuba",    "Estonia",   "France",  "Georgia",   "Germany",   "Greece", "Hong Kong, China", "Hungary",   "Italy",   "Japan",   "Latvia",  "Lithuania", "Mexico",   
"New Zealand",   "Norway",  "Panama",  "Paraguay",  "Poland",  "Portugal",  "Romania",  
"Russia",  "Singapore", "Slovenia",  "South Africa",  "South Korea",   "Spain",   "Sweden",   
"Switzerland",   "Turkey",  "Ukraine",   "United Kingdom",   "United States", "Uruguay"], 
  { value: ["United States"], 
    label: "Nationalities:"
  }
)

filteredDataSub = transpose(dataCSR).filter(function(dancer) {
  return compNS.includes(dancer.Nationality);
})

Plot.plot({
y: {
    grid: true,
    domain: [0, 150]
  },
  marks: [
    Plot.line(filteredDataSub, {
      x: "Year",
      y: "# of Submitters",
      stroke: "Nationality",
      strokeWidth: 2,
      curve: "linear"
    }),
    Plot.dot(filteredDataSub, {
      x: "Year",      // feature for the x channel
      y: "# of Submitters",     // feature for the y channel
      fill: "Nationality",  // feature for the stroke
    }), 
  ],
  width: 554, 
  height: 280, 
  x: { ticks: 3 }, 
  marginLeft: 50, 
  color: {
    legend: true,
    width: 554,
    columns: "120px"
  }
})

* This data is only available from 2020 to 2024.

Competitors

viewof compN = Inputs.checkbox(
  ["Argentina", "Australia", "Belarus",   "Belgium",   "Brazil",  "Bulgaria",  "Canada",   
"China",   "Cuba",    "Estonia",   "France",  "Georgia",   "Germany",   "Greece", "Hong Kong, China", "Hungary",   "Italy",   "Japan",   "Latvia",  "Lithuania", "Mexico",   
"New Zealand",   "Norway",  "Panama",  "Paraguay",  "Poland",  "Portugal",  "Romania",  
"Russia",  "Singapore", "Slovenia",  "South Africa",  "South Korea",   "Spain",   "Sweden",   
"Switzerland",   "Turkey",  "Ukraine",   "United Kingdom",   "United States", "Uruguay"], 
  { value: ["United States"], 
    label: "Nationalities:"
  }
)

filteredData = transpose(dataCR).filter(function(dancer) {
  return compN.includes(dancer.Nationality);
})

Plot.plot({
y: {
    grid: true,
    domain: [0, 25]
  },
  marks: [
    Plot.line(filteredData, {
      x: "Year",
      y: "# of Competitors",
      stroke: "Nationality",
      strokeWidth: 2,
      curve: "linear"
    }),
    Plot.dot(filteredData, {
      x: "Year",      // feature for the x channel
      y: "# of Competitors",     // feature for the y channel
      fill: "Nationality",  // feature for the stroke
    }), 
  ],
  width: 554, 
  height: 280, 
  x: { ticks: 3 }, 
  marginLeft: 50, 
  color: {
    legend: true,
    width: 554,
    columns: "120px"
  }
})

Finalists

viewof compNF = Inputs.checkbox(
  ["Argentina", "Australia", "Belarus",   "Belgium",   "Brazil",  "Bulgaria",  "Canada",   
"China",   "Cuba",    "Estonia",   "France",  "Georgia",   "Germany",   "Greece", "Hong Kong, China", "Hungary",   "Italy",   "Japan",   "Latvia",  "Lithuania", "Mexico",   
"New Zealand",   "Norway",  "Panama",  "Paraguay",  "Poland",  "Portugal",  "Romania",  
"Russia",  "Singapore", "Slovenia",  "South Africa",  "South Korea",   "Spain",   "Sweden",   
"Switzerland",   "Turkey",  "Ukraine",   "United Kingdom",   "United States", "Uruguay"], 
  { value: ["United States"], 
    label: "Nationalities:"
  }
)

filteredDataF = transpose(dataCFR).filter(function(dancer) {
  return compNF.includes(dancer.Nationality);
})

Plot.plot({
y: {
    grid: true,
    domain: [0, 10]
  },
  marks: [
    Plot.line(filteredDataF, {
      x: "Year",
      y: "# of Finalists",
      stroke: "Nationality",
      strokeWidth: 2,
      curve: "linear"
    }),
    Plot.dot(filteredDataF, {
      x: "Year",      // feature for the x channel
      y: "# of Finalists",     // feature for the y channel
      fill: "Nationality",  // feature for the stroke
    }), 
  ],
  width: 554, 
  height: 280, 
  x: { ticks: 3 }, 
  marginLeft: 50, 
  color: {
    legend: true,
    width: 554,
    columns: "120px"
  }
})

Prizewinners

viewof compP = Inputs.checkbox(
  ["Argentina", "Australia", "Belarus",   "Belgium",   "Brazil",  "Bulgaria",  "Canada",   
"China",   "Cuba",    "Estonia",   "France",  "Georgia",   "Germany",   "Greece", "Hong Kong, China", "Hungary",   "Italy",   "Japan",   "Latvia",  "Lithuania", "Mexico",   
"New Zealand",   "Norway",  "Panama",  "Paraguay",  "Poland",  "Portugal",  "Romania",  
"Russia",  "Singapore", "Slovenia",  "South Africa",  "South Korea",   "Spain",   "Sweden",   
"Switzerland",   "Turkey",  "Ukraine",   "United Kingdom",   "United States", "Uruguay"], 
  { value: ["United States"], 
    label: "Nationalities:"
  }
)

filteredDataP = transpose(dataCPR).filter(function(dancer) {
  return compP.includes(dancer.Nationality);
})

Plot.plot({
y: {
    grid: true,
    domain: [0, 5]
  },
  marks: [
    Plot.line(filteredDataP, {
      x: "Year",
      y: "# of Prizewinners",
      stroke: "Nationality",
      strokeWidth: 2,
      curve: "linear"
    }),
    Plot.dot(filteredDataP, {
      x: "Year",      // feature for the x channel
      y: "# of Prizewinners",     // feature for the y channel
      fill: "Nationality",  // feature for the stroke
    }), 
  ],
  width: 554, 
  height: 280, 
  x: { ticks: 3 }, 
  marginLeft: 50, 
  color: {
    legend: true,
    width: 554,
    columns: "120px"
  }
})

* Competitors who win more than one prize are only counted once. This data is only available from 2020 to 2024.

In dancer submissions, 2024 was the first year in our studied period where the United States had the highest number of submissions, greater than the usual highest (Japan). This steady increase in the number of United States submissions seems to have translated to competitors. In the largest contributors of competitors, the only consistent trend is a gradual increase in the number of competitors from the United States between 2018 and 2024. Despite this, the number of finalists for major contributing countries is mostly stagnant with minor year to year fluctuations.

4.2.4 What schools produce the most Prix de Lausanne participants, finalists, or prizewinners?

As a highly selective competition, having dancers selected to compete in the Prix is another useful metric for identifying quality training programs that can be used alongside how many dancers find positions in ballet companies (see our other section on placements).

4.2.4.1 Training Schools By Group

Competitors

As mentioned earlier, competitors are divided into age and gender groups. ‘Group A’ roughly corresponds to ages 14 to 16 years old during the competition while ‘Group B’ is 17 to 18 years old. Age is determined by date of birth, accounting for countries which used different age systems in the past such as South Korea and China. Looking at training schools by group, we see large differences in participant categories from training schools. For example, the large majority of participants from the major South Korean training schools Seoul Arts High School and SunHwa Middle & High School of the Arts are in the ‘Girls’ categories. While participants listing SunHwa Middle & High School of the Arts are mostly in ‘Girls A’, the reverse is true for Seoul Arts High School with most in ‘Girls B’.

Next, let’s look at how schools differ by other categories. We have a more limited data set so the following table will only cover 2020 to 2024 rather than 2018 to 2024 like the previous table. There are some notable details on how schools are recorded. Some of the entries are repeat competitors who may have changed their primary training school since the last time they competed. The majority of participants list one school as there training school but there are instances where dancers list more than one. In this case, all schools listed were counted.

172 different schools were listed as a primary training school for a competitor. Many schools have only sent one dancer to Prix de Lausanne since 2018 (105 schools - 61.05% of all schools). This follows a trend in training schools for dancers who end up in larger US ballet companies–a few schools are very successful in this metric while a larger number of schools make up the rest (from our previously mentioned school section).

Some participants compete multiple times. From 2018 to 2024, 37 dancers have competed in Prix de Lausanne more than once (6.84% of all competitors). Does repeat competition lead to higher chances of advancing or placement?

4.2.5 What competitor category most commonly wins prizes?

Prix de Lausanne awards prizes in a ranked manner that is not strictly split by category. This means that dancers of different ages and gender largely compete for the same pool of prizes. Younger contestants win prizes less frequently than older dancers (Group A vs Group B). The amount of prizes across the BOYS and GIRLS categories is roughly equal when considering all prizes. However, the grand prize has been awarded to a competitor in the BOYS category since 2020.

4.2.6 What are the most common placements for prize winners?

How useful is the Prix de Lausanne for finding positions in prestigious schools, second companies, or main companies? Here, we will look at the public results of the networking forum and the prizewinners. The networking forum is designed for dancers who may not have made it to the broadcasted finals or have won prizes to make connections with directors and find opportunities.

Again, compare results with data from our data on top US ballet companies for schools and second companies. Learn more about main companies here and here.

In a few cases, dancers were listed in both lists. This was addressed so overlaps were only counted once unless these are two different offers from two different times competing. Since 2020, one dancer competed two separate years and received placement offers both times–the first time from the networking forum and the second from winning a prize.

4.2.6.1 Placements From The Networking Forum or Prizes By Program Type

Networking Forum

Placement Type	#
S	86
C	7
SC	6
S = School, SC = Second Company, C = Company

Prizewinners

Placement Type	#
C	15
S	15
SC	11
S = School, SC = Second Company, C = Company

4.2.6.2 Placements By Group

Networking Forum

S = School, SC = Second Company, C = Company.
This data is only available from 2020 to 2024.

Prizewinners

S = School, SC = Second Company, C = Company.
* Competitors who win more than one prize are only counted once. This data is only available from 2020 to 2024.

4.2.7 How does preselection impact a competitors chance of winning a prize or placement?

Preselection events are regional competitions or workshops hosted by the Prix de Lausanne organization. Preselection events hosted by the Prix de Lausanne organization provide opportunities to participate in the main competition in Switzerland if chosen. Historically, preselection also involved inviting competitors from other major competitions (such as Youth America Grand Prix) to compete in Lausanne as well. Directly affiliated preselection events have greatly expanded in recent years with multiple events a year in different geographical regions.

Public reports on preselected candidates from 2018 and 2019 editions of the competition include numbers but not preselected candidate information. Thus, 2018 and 2019 preselection data is excluded from our analysis.

Given this special process, are preselection participants more likely to win a prize than video round participants?

4.2.7.1 Number of Preselected Candidates By Group