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Month: January 2021

Fehlerfrei – a quick article on shooting clean

Posted on 2021-01-08 | by biathlonanalytics | Leave a Comment on Fehlerfrei – a quick article on shooting clean

The Germans have a great word they use for shooting clean in biathlon: Fehlerfrei. The visual below is a brief look into shooting Fehlerfrei and how it relates to shooting and shot times. The interactive version allows you to filter to only men or women, by default both are included.

After reviewing shootings based on over 200,000 shots we can see just over 39% of shootings are Fehlerfrei. The men and woman have a very similar percentage.

When looking at the biathlon nations and Canada, we can see there is not much difference between Canadian men and women, but for the Swedes the women better than the men, where for the French and Norwegians the men do better than the women:

For men and women combined, the Norwegians are doing best with regards to the Fehlerfrei percentage, but the Canadians are the fastest shooters of the group.

Now let’s look at the same data split between men and women:

For the men (left) the Norwegians (red) are shooting clean over 50% of the time, but have been taking more time in the current season compared to previous seasons. The Canadians are improving from last season in both categories but are still on the low end (~35%) of the Fehlerfrei %, even though their shooting is still very fast.

The women (right) tell a different story; Germany, Sweden en Norway are heading exactly in the direction you want to go: bottom right of the chart (which means quick shooting and large percentage of Fehlerfrei shooting). In that second category Canada was in the wrong area in the last two seasons but heading in the right direction this season. Russia is going in the opposite direction.

If these trends continue this season, the Canadian women are looking promising. Although it should be noted, as shown in the first chart, the Canadians have a lot fewer shots as they have less athletes participating, so the success of individuals has a larger and more direct impact on the nation’s data/

The Tableau report contains more details and I am planning to do further analysis in R, depending on time availability. If you have any feedback or suggestions, please leave a comment below.

RJ

Posted in Statistical analysis | Tagged Fehlerfrei, shooting clean

Shooting Efficiency comparison: First trimester 2019–20 vs. First trimester 2020–21

Posted on 2021-01-07 | by real biathlon | Leave a Comment on Shooting Efficiency comparison: First trimester 2019–20 vs. First trimester 2020–21

Following up on my last post on skiing speed, this is a comparison of overall shooting quality between trimester 1 of last season and trimester 1 this winter. Shooting Efficiency is an attempt to combine shooting accuracy and shooting time. For more details how it’s calculated, see here.

If you can’t find a specific athlete, you can always look up the complete World Cup field (also available per trimester) for the ongoing season (as well as all previous seasons) here:

  • 2020–21 Shooting Efficiency: Men | Women

Note: Only athletes with at least 4 non-team races in trimester 1 of both the previous and the current season are included in the tables below. Shooting Efficiency is an overall shooting score, combining shooting accuracy and shooting time. It is the theoretical average time an athlete loses through shooting (based on hit rate, range time and potential penalty loops)


Men

Erik Lesser is the most improved shooter among regular starters, losing 2:06.6 min in a theoretical sprint at the range. He has always been fast, which hasn’t changed this year, however, his accuracy is currently at a career high (87.9%). Simon Eder is the best shooter overall (incredible 96.4% hit rate), also much improved over last year. Sturla Holm Lægreid isn’t far behind (1:56.2 min) – he doesn’t show up in the table, because his first World Cup race was in March. Johannes Thingnes Bø has been struggling with his shooting so far, his accuracy is down 4.2% (albeit on a very high level), plus he shoots 2.0s slower.

Changes in Shooting Efficiency compared to 2019–20 | World Cup Trimester 1

NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
1LesserErikGER
987.8650.021.92:06.6-35.1
2GuigonnatAntoninFRA
990.7152.722.92:06.6-21.5
3StroliaVytautasLTU
780.0055.022.92:35.7-21.4
4EderSimonAUT
996.4349.323.11:46.9-19.4
5SamuelssonSebastianSWE
989.2953.321.32:09.4-16.6
6HasillaTomasSVK
677.5053.723.82:40.9-14.2
7MoravecOndrejCZE
987.1450.622.72:10.3-13.8
8PonsiluomaMartinSWE
981.4350.121.42:20.0-11.4
9NelinJesperSWE
975.7153.321.72:39.3-11.2
10KrcmarMichalCZE
990.0053.621.42:08.5-10.3
11WegerBenjaminSUI
986.4352.622.52:15.8-9.0
12ErmitsKalevEST
876.6753.322.92:39.9-8.4
13GaranichevEvgeniyRUS
593.7552.024.41:59.2-6.2
14DombrovskiKarolLTU
787.0056.622.72:22.7-5.6
15SinapovAntonBUL
680.0051.524.72:32.4-5.2
16HoferLukasITA
982.8651.520.02:17.4-5.1
17FakJakovSLO
992.1451.721.42:00.3-2.0
18DollBenediktGER
987.1450.822.02:09.9-1.7
19Fillon MailletQuentinFRA
992.1450.121.61:57.3-1.0
20ChristiansenVetle SjaastadNOR
990.0053.621.82:09.0-0.4
21FemlingPeppeSWE
881.6750.122.52:21.5-0.1
22NordgrenLeifUSA
681.2553.724.12:32.6-0.0
23PrymaArtemUKR
880.0050.923.12:28.1+1.4
24LatypovEduardRUS
985.0055.121.92:22.9+1.8
25BormoliniThomasITA
587.1452.921.82:13.8+1.9
26HiidensaloOlliFIN
676.2555.723.12:46.1+2.1
27ClaudeFabienFRA
980.0050.921.02:23.8+2.5
28RastorgujevsAndrejsLAT
880.8353.721.42:28.4+3.5
29DohertySeanUSA
880.0052.122.72:29.5+5.4
30JacquelinEmilienFRA
990.0049.220.41:58.7+6.1
31GowScottCAN
676.2550.723.62:37.5+6.2
32EberhardJulianAUT
878.3352.321.62:31.3+6.5
33PidruchnyiDmytroUKR
880.0049.222.52:23.5+9.2
34YaliotnauRamanBLR
769.0054.222.92:59.6+9.6
35PeifferArndGER
790.0052.421.92:06.6+10.6
36DaleJohannesNOR
987.1456.322.22:21.0+11.1
37SeppalaTeroFIN
878.3353.421.52:33.3+11.4
38DesthieuxSimonFRA
983.5751.621.52:18.7+11.6
39DovzanMihaSLO
686.2549.424.12:11.8+11.9
40TkalenkoRuslanUKR
576.6749.422.72:31.7+13.0
41KuehnJohannesGER
880.0056.020.72:33.4+13.1
42LeitnerFelixAUT
784.0058.222.62:32.6+13.3
43IlievVladimirBUL
675.0054.522.02:44.2+13.4
44BjoentegaardErlendNOR
585.7155.720.92:21.1+14.7
45VaclavikAdamCZE
673.7556.123.52:54.0+14.8
46BocharnikovSergeyBLR
880.8353.424.82:34.3+16.4
47GuzikGrzegorzPOL
772.0052.422.72:48.5+17.4
48WindischDominikITA
575.7155.220.92:41.1+17.6
49TrsanRokSLO
685.5649.823.42:13.4+18.0
50EliseevMatveyRUS
990.0049.722.52:01.8+18.5
51BoeTarjeiNOR
985.7153.520.62:16.4+18.8
52BoeJohannes ThingnesNOR
987.8652.021.32:09.8+20.8
53LangerThierryBEL
781.0054.522.32:31.5+23.5
54DudchenkoAntonUKR
680.0055.324.42:39.6+25.9
55ClaudeFlorentBEL
677.5058.521.82:46.2+29.5
56BauerKlemenSLO
774.0049.923.22:40.1+30.4
57LoginovAlexanderRUS
985.0051.722.42:16.9+30.8
58StvrteckyJakubCZE
764.0058.821.73:15.7+38.8


Women

Suvi Minkkinen is the most improved among women – she had a horrible December 2019, where she only managed to hit 66.0% of her targets. World Cup leader, Marte Olsbu Røiseland, is currently 11.9% more accurate than during trimester 1 last season. The results for Hanna Öberg (best shot overall) haven’t changed much, neither has the efficiency of Dorothea Wierer; her problems are almost exclusively skiing-related. Denise Herrmann is roughly 10s faster overall at the range (but in a sprint her 1.5% slower skiing loses her almost twice as much on the tracks).

Changes in Shooting Efficiency compared to 2019–20 | World Cup Trimester 1

NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
NoFamily NameGiven NameNationRacesHit RateRange TimePenalty LoopTime Loss
Sprint
Change
1MinkkinenSuviFIN
785.0052.725.92:24.3-46.1
2FrolinaAnnaKOR
676.2555.725.72:52.4-42.3
3KadevaDanielaBUL
585.0054.927.12:30.4-32.0
4KocerginaNataljaLTU
580.0057.126.82:47.8-31.1
5DunkleeSusanUSA
878.3355.826.22:48.4-28.2
6RoeiselandMarte OlsbuNOR
988.5752.223.92:11.8-26.8
7ReidJoanneUSA
683.751:00.725.12:42.1-20.3
8ColomboCarolineFRA
881.6754.425.52:35.6-19.1
9TomingasTuuliEST
783.0058.025.32:39.0-17.2
10TachizakiFuyukoJPN
885.0059.225.92:37.2-16.2
11AlimbekavaDzinaraBLR
990.0054.724.02:13.4-15.4
12LunderEmmaCAN
991.4352.525.12:06.6-14.2
13CadurischIreneSUI
682.5049.525.82:24.3-13.4
14OebergElviraSWE
986.4353.424.52:20.1-13.0
15SemerenkoValentinaUKR
590.0052.825.62:11.2-11.6
16HerrmannDeniseGER
983.5754.223.72:27.4-10.4
17KlemencicPolonaSLO
675.0056.725.92:58.1-9.0
18HinzVanessaGER
786.0055.125.42:25.7-8.6
19SchwaigerJuliaAUT
787.0056.725.32:26.3-8.0
20KnottenKaroline OffigstadNOR
991.4351.125.52:04.0-7.8
21LescinskaiteGabrieleLTU
585.001:01.125.72:40.7-6.7
22OebergHannaSWE
990.0049.624.32:03.5-5.1
23DavidovaMarketaCZE
982.1457.124.02:37.0-4.4
24KryukoIrynaBLR
791.251:00.325.22:22.6-3.6
25Braisaz-BouchetJustineFRA
980.7156.622.92:37.5-2.7
26WiererDorotheaITA
991.4351.324.52:03.6-1.2
27Hojnisz-StaregaMonikaPOL
686.6755.725.12:24.8-1.1
28GasparinElisaSUI
782.0053.125.12:31.3-0.3
29EganClareUSA
985.0059.224.32:34.8+2.1
30HaeckiLenaSUI
879.1750.324.92:32.6+2.8
31MironovaSvetlanaRUS
678.7555.424.62:43.0+4.9
32TodorovaMilenaBUL
876.6756.824.72:51.2+5.7
33GasparinAitaSUI
784.0053.726.22:29.3+7.4
34HauserLisa TheresaAUT
984.2953.623.42:24.1+7.9
35BescondAnaisFRA
982.8657.823.62:36.1+8.0
36SimonJuliaFRA
982.8650.124.82:22.7+8.6
37BrorssonMonaSWE
885.8354.424.82:24.0+8.8
38SolaHannaBLR
870.7752.924.32:56.8+9.0
39VittozziLisaITA
982.1454.323.92:31.3+9.9
40PreussFranziskaGER
986.4351.823.62:15.7+10.7
41EckhoffTirilNOR
984.2955.723.72:28.5+11.0
42BeaudrySarahCAN
778.0052.626.42:43.4+13.2
43SanfilippoFedericaITA
575.7158.425.62:58.9+13.2
44ZukKamilaPOL
778.001:01.125.72:58.7+14.0
45EderMariFIN
673.751:01.724.93:08.7+14.1
46TandrevoldIngrid LandmarkNOR
985.7156.423.82:26.8+14.2
47OjaReginaEST
571.6753.724.52:56.9+14.4
48PuskarcikovaEvaCZE
780.0051.327.42:37.3+14.4
49JislovaJessicaCZE
778.0057.225.92:51.4+15.6
50GasparinSelinaSUI
675.0058.024.42:57.0+15.7
51BlashkoDaryaUKR
992.1456.926.02:14.1+17.0
52PerssonLinnSWE
985.0054.324.62:25.5+19.9
53DzhimaYuliiaUKR
786.0056.925.22:29.0+20.1
54VoroninaTamaraRUS
584.0052.226.62:27.1+21.2
55PidhrushnaOlenaUKR
584.2955.726.52:33.0+21.2
56ZbylutKingaPOL
774.0056.724.72:57.6+24.1
57CharvatovaLucieCZE
763.7552.123.93:10.9+28.1
58TalihaermJohannaEST
676.251:00.826.33:03.9+31.3
59ChevalierChloeFRA
879.1758.824.52:48.7+32.6
60InnerhoferKatharinaAUT
865.8355.623.93:12.8+38.0

Posted in Statistical analysis | Tagged 2019–20 season, 2020–21 season, shooting

Ski speed comparison: First trimester 2019–20 vs. First trimester 2020–21

Posted on 2021-01-05 | by real biathlon | 2 Comments on Ski speed comparison: First trimester 2019–20 vs. First trimester 2020–21

In the past, I often compared changes in ski speed to the entire previous season, which might be slightly deceiving, especially at the beginning of a season. Some athletes start particularly strong before Christmas, others might do better late in a season, when parts of the field are already tired. Potentially, it’s a better idea to only compare the same World Cup period. Here I did this specifically for World Cup trimester 1 (roughly December 2019 vs. December 2020).

I used the percentage back from the top 30 median for this. Comparisons against the entire field are problematic this year, because of temporary rule changes (each National Federation can register two athletes who have not fulfilled the qualification criteria). That makes the whole field slightly slower.

If you can’t find a specific athlete, you can always look up the complete World Cup field (also available per trimester) for the ongoing season (as well as all previous seasons) here:

  • Ski speed: Men | Women

Note: Only athletes with at least 5 non-team races in trimester 1 of both the previous and the current season are included in the tables below. “Back from Top30 median” is the percentage back from each race’s top 30 median Course Time (arithmetic mean per season).


Men

Martin Ponsiluoma and Sebastian Samuelsson are the most improved overall. Both lowered their average ski rank by at least 24 positions, plus they are skiing 3.5% and 3.0% faster respectively (compared to their speed last December). Veteran Jakov Fak (his 15th World Cup season) is currently on his best ski form in 5 years. World Cup leader, Johannes Thingnes Bø, is very close to his level from last December (he is slightly ahead).

Quentin Fillon Maillet started the season as one of the favorites, but he is not at his peak form yet; his hit rate is at a career high right now, but his speed has declined a lot compared to December 2019 (average ski rank almost doubled). For this pre-Christmas period, Germany and France have only one athlete each who improved season-to-season: Erik Lesser (only started in 4 races in December 2019) and Fabien Claude, respectively.

Changes in Ski Speed compared to 2019–20 season | World Cup Trimester 1

NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
1PonsiluomaMartinSWE
95.7-24.3-1.66-3.49
2SamuelssonSebastianSWE
98.2-25.9-1.13-2.99
3LatypovEduardRUS
918.9-26.3+0.40-2.78
4BocharnikovSergeyBLR
827.5-21.8+1.06-2.49
5FakJakovSLO
920.0-17.5+0.46-1.84
6ChristiansenVetle SjaastadNOR
913.3-12.8-0.63-1.67
7DaleJohannesNOR
96.1-8.9-1.97-1.61
8LangerThierryBEL
751.7-8.7+3.72-1.48
9NelinJesperSWE
912.2-14.2-0.44-1.44
10DombrovskiKarolLTU
761.1-11.5+4.84-1.40
11DohertySeanUSA
849.6-11.7+3.64-1.17
12EliseevMatveyRUS
935.8+4.3+2.15-1.14
13WegerBenjaminSUI
920.2-7.8+0.58-0.87
14LoginovAlexanderRUS
913.7-5.9-0.25-0.76
15GuzikGrzegorzPOL
756.4-11.2+4.29-0.73
16NordgrenLeifUSA
651.8-3.3+3.60-0.67
17GaranichevEvgeniyRUS
533.8-8.2+2.27-0.60
18TrsanRokSLO
667.0-12.8+5.98-0.41
19BoeJohannes ThingnesNOR
92.4-2.1-2.66-0.24
20VaclavikAdamCZE
643.8-1.0+2.78-0.22
21PrymaArtemUKR
836.0+1.0+2.13-0.13
22ClaudeFabienFRA
910.2-1.8-0.68-0.13
23DovzanMihaSLO
678.7+2.5+7.08-0.13
24HiidensaloOlliFIN
647.3-0.7+3.27-0.12
25BoeTarjeiNOR
94.6-1.4-1.79-0.08
26GuigonnatAntoninFRA
931.0+1.7+1.60+0.06
27FemlingPeppeSWE
851.9-2.1+3.65+0.11
28BormoliniThomasITA
544.4+2.0+2.75+0.15
29BjoentegaardErlendNOR
512.6+0.5-0.43+0.19
30PeifferArndGER
718.0+2.2+0.17+0.20
31JacquelinEmilienFRA
910.2+0.9-0.81+0.22
32StvrteckyJakubCZE
733.0+4.0+1.69+0.23
33SeppalaTeroFIN
830.3+4.8+1.39+0.31
34BauerKlemenSLO
747.9-0.9+4.05+0.33
35RastorgujevsAndrejsLAT
820.6+4.1+0.49+0.54
36KrcmarMichalCZE
932.1+9.7+1.59+0.57
37HoferLukasITA
913.3+1.8-0.25+0.62
38DudchenkoAntonUKR
660.2+7.3+4.55+0.75
39EderSimonAUT
939.2+6.2+2.58+0.78
40LeitnerFelixAUT
736.4+8.4+2.00+0.79
41ClaudeFlorentBEL
646.0+14.3+3.05+0.79
42DollBenediktGER
916.0+5.6+0.00+0.84
43SinapovAntonBUL
668.2-1.3+6.13+0.91
44StroliaVytautasLTU
749.4+5.6+3.83+0.94
45Fillon MailletQuentinFRA
911.0+4.1-0.64+1.28
46WindischDominikITA
534.4+13.7+1.87+1.46
47EberhardJulianAUT
821.6+10.8+0.70+1.51
48ErmitsKalevEST
858.0+10.0+4.88+1.67
49KuehnJohannesGER
821.6+14.3+0.57+1.74
50PidruchnyiDmytroUKR
843.5+20.9+2.99+1.82
51DesthieuxSimonFRA
919.6+11.0+0.45+1.98
52TkalenkoRuslanUKR
575.4+20.4+6.16+2.22
53IlievVladimirBUL
641.8+23.3+2.61+2.39


Women

Among regular starters, Dzinara Alimbekava is by far the most improved, 5.0% faster than last season (plus her average ski rank is now an incredible 44.8 positions lower!). Lisa Theresa Hauser and Franziska Preuß stood out more for their (fast) shooting in the past, but they are both among the top 15 skiers at the moment. Hauser’s transformation is particularly remarkable (over 2% faster than any of her previous 7 seasons), Preuß was close to this level before (five years ago).

World Cup leader and overall fastest skier, Marte Olsbu Røiseland, is missing in the table below, because she only appeared in 4 races in December 2019. She is actually slower compared to her speed at the start of the 2019–20 season (+0.5%). Last year’s top skier, Denise Herrmann, is struggling to get going; her average ski rank in trimester 1 last season was 4.5, now it’s 8.3. Dorothea Wierer was the 9th fastest skier last season, now she’s ranked 23rd.

Changes in Ski Speed compared to 2019–20 season | World Cup Trimester 1

NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
NoFamily NameGiven NameNationRacesSki Rank
(avg)
Changeback from
Top30 median
(in %)
Change
1AlimbekavaDzinaraBLR
914.2-44.8-0.33-5.02
2BeaudrySarahCAN
758.9-21.1+4.54-4.11
3ChevalierChloeFRA
822.6-34.7+0.99-3.25
4BlashkoDaryaUKR
943.9-12.1+3.64-2.65
5HauserLisa TheresaAUT
912.7-24.0-0.36-2.47
6OebergElviraSWE
96.7-12.3-1.64-2.33
7PreussFranziskaGER
912.1-8.4-0.56-1.01
8GasparinElisaSUI
745.6-9.2+3.09-0.99
9SchwaigerJuliaAUT
742.9-8.7+2.89-0.94
10EganClareUSA
928.1-8.7+1.25-0.93
11BescondAnaisFRA
916.0-10.5+0.16-0.85
12KnottenKaroline OffigstadNOR
932.9-3.7+1.89-0.79
13CadurischIreneSUI
667.2-2.8+6.03-0.53
14OebergHannaSWE
912.0-1.8-0.72-0.49
15ColomboCarolineFRA
828.4-5.5+1.43-0.41
16TandrevoldIngrid LandmarkNOR
97.1-4.6-1.28-0.29
17PerssonLinnSWE
919.3-3.1+0.33-0.28
18LunderEmmaCAN
937.1-3.7+2.41-0.26
19GasparinSelinaSUI
619.7-1.7+0.44-0.14
20GasparinAitaSUI
747.6+5.1+3.48-0.06
21DavidovaMarketaCZE
96.9-1.1-1.43-0.02
22CharvatovaLucieCZE
722.8+1.1+0.84+0.08
23JislovaJessicaCZE
752.0+3.5+3.77+0.16
24SimonJuliaFRA
919.3+5.6-0.15+0.17
25VoroninaTamaraRUS
568.8+7.4+5.44+0.23
26TalihaermJohannaEST
655.7+0.2+4.88+0.30
27BrorssonMonaSWE
828.9+2.7+1.55+0.35
28InnerhoferKatharinaAUT
821.8+1.4+0.83+0.39
29Braisaz-BouchetJustineFRA
95.8-0.2-1.62+0.48
30ZukKamilaPOL
734.9+6.9+1.99+0.58
31EckhoffTirilNOR
95.6+1.3-1.94+0.73
32VittozziLisaITA
931.4+8.0+1.82+0.84
33OjaReginaEST
576.0+11.0+6.70+0.89
34KryukoIrynaBLR
737.5+11.8+2.20+1.02
35HerrmannDeniseGER
98.3+3.8-1.25+1.09
36ZbylutKingaPOL
761.4+8.9+5.21+1.25
37TachizakiFuyukoJPN
848.3+7.5+3.91+1.33
38EderMariFIN
618.7+7.2+0.30+1.35
39KocerginaNataljaLTU
583.0+14.0+7.64+1.35
40WiererDorotheaITA
921.1+12.0+0.61+1.66
41HinzVanessaGER
745.0+17.0+3.03+1.77
42MironovaSvetlanaRUS
625.5+15.1+1.02+1.79
43SanfilippoFedericaITA
550.8+12.6+4.02+1.85
44SemerenkoValentinaUKR
555.5+17.5+4.22+2.04
45PidhrushnaOlenaUKR
543.6+19.0+3.21+2.14
46HaeckiLenaSUI
832.3+22.7+1.86+2.62
47PuskarcikovaEvaCZE
752.4+27.4+4.42+3.39

One thing that stands out is how well Norway and Sweden are doing (both men and women). Every athlete of those two nations either improved (often significantly), or at least managed to maintain their previous level; not a single athlete got notably worse.

Posted in Statistical analysis | Tagged 2019–20 season, 2020–21 season, skiing

Biathlon Analysis Videos on German TV

Posted on 2021-01-04 | by biathlonanalytics | Leave a Comment on Biathlon Analysis Videos on German TV

The German ARD Sportschau does some very cool analysis after every biathlon race with the assistance of former athletes Kati Wilhelm or Magdalena Neuner.

They typically compare two athletes side by side, looking at ski-speed and shooting speed and results:

I thought it would be interesting to re-create a similar visualization but with the ability to hand-pick the athletes, so I did exactly that in Tableau:

As stated on the report, note that the filters are only showing relevant data, so if you are looking for something that’s not in the list, make sure to reset all filters to show all.

Assuming people watching the analysis videos speak German, the report contains all content in both english and German.

Posted in Statistical analysis

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