Method and program for comparing travel ability using leg similarity

Abstract

In wind surfing in which course selection is changed by a participant in accordance with a change in weather environment such as wind and wave with respect to a racing course C, the present invention involves measuring the direction of wind in the racing course C, acquiring travel data including coordinates (position) and date and time related to traveling of each of a preset reference participant PS and a comparative participant PA, and extracting straight line parts (Leg) that are considered to be important by top level participants. Thereafter, the present invention involves calculating (estimating) the degrees R of similarity between Legs of the comparative participant PA and Legs of the reference participant PS on the basis of the direction, distance, and start time of Legs for respective types of travel direction (R (horizontal) / D (down) / U (up)), and evaluating the ability of the comparative participant PA on the basis of the difference between an evaluation value (e.g., 10 s AVE (maximum speed of 10-second averages)) of the comparative participant PA and the evaluation value of the reference participant PS in a Leg having the highest degree of similarity.

Description

Method and program for comparing running ability using leg similarity

[0001] An embodiment of the present invention relates to a running ability comparison method and program using Leg similarity.

[0002] Generally, when comparing sports abilities, it is easy to compare them if the environment is reproducible to the same degree. For example, in the 100m sprint, the same environment can be reproduced indoors or within a standard wind speed, so comparisons can be made based on time, speed, etc.

[0003] On the other hand, in windsurfing, the environment, such as wind speed and direction on the ocean, is constantly changing. Therefore, to compare the abilities of athletes, it is necessary to have them sail side by side in the same environment (same wind speed, direction, wave conditions, etc.), which makes the environmental conditions almost identical and makes comparisons possible.

[0004] However, even when racing side by side in the same environment, the course taken by each racer (points at which they turn) can differ. For this reason, performance comparisons have traditionally been made by comparing the rankings and times for each race (start-goal), or by comparing speeds and other parameters based on the direction of travel (reaching (R), downwind (D), or upwind (U)) during a simulated race.

[0005] In the ability comparison described in Non-Patent Document 1, the characteristics of movement patterns in a simulated windsurfing race were captured, and the characteristics of the top and bottom surfers in the race were examined. Using data on movement patterns during the simulated race, the movement patterns (sailing speed, sailing angle, VMC: Velocity Made good on Course (VMG): Velocity Made Good), sailing distance) for each sailing phase (start / upwind / downwind) were analyzed and evaluated by dividing the surfers into top and bottom groups.

[0006] "Characteristics of Movement Patterns During Simulated Windsurfing Races", Masahiro Hagiwara1,2), Yasumitsu Ishii1), 1) Japan Institute of Sports Sciences, 2) Japan Sailing Federation Olympic Training Committee, Sports Performance Research, 7, 320-333, 2015

[0007] In the ability comparison described in Non-Patent Document 1, since each competitor sails side by side in the same area, it is assumed that there are almost no differences in wind direction or speed. Furthermore, the comparison is made by category (by sailing phase (start / upwind / downwind)). Therefore, strategic elements such as each competitor's course selection within the category are not excluded, and the jibe / tack actions performed during course changes are defined as 10 seconds, and the data during this period is excluded from the speed calculation.

[0008] However, it is a component of each type, and it is not possible to compare abilities based on the straight section (leg), which is more detailed and important to top-level athletes.

[0009] It is possible to prioritize comparisons based on legs, eliminate strategic elements, and fix the course, but in this case there is a problem in that the comparisons will be made under constraints that prevent the competitors from fully demonstrating their true performance in terms of speed, such as sailing speed and VMG.

[0010] The present invention has been made in consideration of these problems, and aims to provide a method and program for comparing running ability using leg similarity, which makes it possible to compare abilities based on the straight section (leg) that top-level athletes consider important, without restricting the athlete's ability to demonstrate their original performance.

[0011] A running ability comparison method using leg similarity according to an embodiment of the present invention causes a control unit of a running ability comparison device to execute the following processes: acquire running data including coordinates and times associated with the running of each of a reference athlete and a comparison athlete, for a course including types of traveling direction corresponding to wind direction; extract the legs run by the reference athlete and the comparison athlete based on the running data; calculate the similarity between the extracted legs of the reference athlete and the comparison athlete for each type of traveling direction; calculate the difference between an evaluation value obtained based on the running data of the leg of the reference athlete with the highest similarity and an evaluation value obtained based on the running data of the leg of the comparison athlete; and output the difference in evaluation values ​​as the evaluation result of the comparison athlete.

[0012] According to the method for comparing running ability using leg similarity according to an embodiment of the present invention, it is possible to compare abilities based on the straight section (leg), which is important to top-level athletes, without being restricted in demonstrating the athletes' original performance.

[0013] FIG. 1 is a diagram illustrating an overview of a running ability comparison device 1 according to an embodiment of the present invention, which relates to a running ability comparison method and program using leg similarity. FIG. 2 is a diagram illustrating an example of differences in the courses (course selection) between a reference player PS and a comparison player PA. FIG. 3 is a functional block diagram illustrating the functional configuration of the running ability comparison device 1 according to the embodiment. FIG. 4 is a block diagram illustrating the hardware configuration of the running ability comparison device 1. FIG. 5 is a flowchart illustrating an example of a running ability comparison process performed by the running ability comparison device 1. FIG. 6 is a diagram illustrating the relationship between a running data acquisition screen G displayed on the display as running data is acquired by the running ability comparison device 1 and the running data stored in the running data storage unit 14. FIG. 7 is a diagram illustrating an example of leg data for each type (R / D / U) of the reference player PS and the comparison players PA, PB, etc., stored in the leg data storage unit 18. FIG. 8 is a diagram illustrating a calculation formula for the similarity between the leg vector "Leg (→a)" of the reference player PS and the leg vector "Leg (→b)" of the comparison players PA, PB, etc. FIG. 9 is a diagram showing a calculation example (Case 1) of the similarity R between the leg vector PS2 (Leg) of the reference player PS and the leg vectors PA2 (Leg 1) / PA3 (Leg 2) / PA4 (Leg 3) of the comparison player PA. FIG. 10 is a diagram showing a calculation example (Case 2) of the similarity R between the leg vector PS2 (Leg) of the reference player PS and the leg vectors PA2 (Leg 1) / PA3 (Leg 2) / PA4 (Leg 3) of the comparison player PA. FIG. 11 is a diagram showing an example of the evaluation value comparison list L created by the similarity estimation unit 19. FIG. 12 is a diagram showing the evaluation value comparison list L created by the evaluation value difference calculation unit 21 by adding, for each evaluation value included in the leg data for each leg of the reference player PS, the differences in the evaluation values ​​included in the leg data for the same leg of each of the comparison players PA, PB, and PC. Figure 13 is a diagram illustrating the relationship between the average value AVE(X) of the differences in the evaluation values ​​of each of the comparison players PA to PD compiled by the evaluation value compilation unit 23 and the ability values ​​(◎ / ○ / △ / ×) for each leg of the direction of travel (R / D / U) of each of the comparison players PA to PD created by the ability value creation unit 25.

[0014] Hereinafter, a driving performance comparison method and program using Leg similarity according to an embodiment will be described with reference to the drawings.

[0015] (Outline of the embodiment) FIG. 1 is a diagram illustrating an outline of a driving performance comparison device 1 according to a driving performance comparison method and program using Leg similarity according to an embodiment.

[0016] The driving performance comparison device 1 using leg similarity is realized by one or more computers (PC: personal computer, tablet terminal, server, etc.) having one or more control units (CPU: central processing unit).

[0017] The running ability comparison device 1 of the embodiment shown in FIG. 1A will be described as a device for comparing the running abilities of athletes in a windsurfing race.

[0018] Here, for example, as shown in Figure 1 (B), the target is a simulated race course C in which the starting line is between a pair of start-goal marks SG set parallel to the wind direction, the course starts from rounding mark a set upwind, goes around rounding mark b set downwind, turns around, and then goes around rounding mark a again to return to the starting line.

[0019] The running ability comparison device 1, for example, acquires coordinate (position) data of each rounding mark (SG, a, b) using a mark coordinate measuring device (GPS: Global Positioning System) attached to each rounding mark, acquires data on wind direction and wind speed on the race course C using a wind direction and wind speed measuring device attached to a motor board BC near the start line, and acquires coordinate (position) data accompanying the running (movement) of each board (PS, PA-PC) as running data using a player coordinate measuring device attached to each player's board (PS, PA, PB, PC).

[0020] The running ability comparison device 1 compares the running ability of a reference athlete's board PS (hereinafter also referred to as the reference athlete PS), which is arbitrarily set among multiple athletes participating in a simulated race, with the running ability of a comparison athlete's boards PA, PB, PC (hereinafter also referred to as the comparison athlete PA, PB, PC), and outputs an ability evaluation result, which is the comparison result.

[0021] The unit of comparison is the straight section (Leg) for each type of direction of travel during the simulated race (R (Reaching) / D (Down) / U (Up)).

[0022] As shown in Figure 1(B), even if the straight sections of the course taken by the reference athlete PS are Legs (1) to (6), the courses of each athlete (PS, PA, PB, PC) are strategic, and therefore they do not necessarily run the same course between the rounding marks (SG, a, b).

[0023] FIG. 2 is a diagram showing an example of the difference in course (course taking) between the reference player PS and the comparative player PA.

[0024] For example, as shown in Figure 2 (A), on the downhill (D), the course taken by the reference player PS from the rounding (upper) mark a to the rounding (lower) mark b is PS2, PS3, while the courses taken by the comparison player PA are PA2, PA3, PA4, PA5, and the number and length (distance) of legs differ depending on the timing and number of tacking.

[0025] Also, for example, as shown in Figure 2 (B), on the uphill (U) course, the courses PA1 and PA2 of the reference player PS differ from the courses PS1 and PS2 taken by the reference player PS in terms of leg direction (P: Port / S: Starboard) due to the difference in the tack direction immediately after passing the rounding (bottom) mark b.

[0026] In the running ability comparison device 1, the unit for comparing running ability is the straight section (Leg) for each type of direction of travel during the simulated race (R (sideways) / D (downhill) / U (uphill)), but since each athlete's U (uphill) / D (downhill) course (course selection) is strategic and the course is different for each athlete, it is not possible to simply determine the Leg of the comparison athlete PA in order of the Leg of the reference athlete PS.

[0027] Therefore, the running ability comparison device 1 extracts and compares the legs of each of the comparison players PA, PB, and PC that are highly similar to the leg of the reference player PS, excluding jibe / tuck movements, for each type of direction of travel (R / D / U), based on the running data acquired while the reference player PS and the comparison players PA, PB, and PC are running side by side on the race course C.

[0028] Here, a leg of a comparison player that is highly similar to a leg of a reference player PS is one in which each leg is in the same direction and distance and has a similar start time.

[0029] (Configuration of the embodiment) FIG. 3 is a functional block diagram showing the configuration of functions possessed by the driving performance comparison device 1 of the embodiment.

[0030] The driving ability comparison device 1 includes a skill capture / analysis unit 10, a comparison unit 20, and a presentation unit 30.

[0031] The skill capture / analysis unit 10 includes a driving data acquisition unit 11, a navigation mark coordinate input unit 12, a date and time input unit 13, a driving data storage unit 14, a Leg extraction unit 15, an R / D / U judgment value input unit 16, a type judgment unit 17, a Leg data storage unit 18, and a similarity estimation unit 19.

[0032] The comparison unit 20 includes an evaluation value difference calculation unit 21 , a comparison Leg data storage unit 22 , an evaluation value collection unit 23 , a capacity judgment value input unit 24 , and a capacity value creation unit 25 .

[0033] The presentation unit 30 includes an ability value presentation unit 31 .

[0034] In the skill capture and analysis unit 10, the running data acquisition unit 11 acquires data on wind direction and speed on the race course C and coordinate (position) data associated with the running (movement) of each player (PS, PA, PB, PC).

[0035] The roundabout mark coordinate input unit 12 inputs data on the coordinates (position) of the roundabout mark (SG, a, b).

[0036] The date and time input unit 13 inputs date and time data for obtaining the start-finish time (start time, finish time) of each leg associated with the running (movement) of each athlete (PS, PA, PB, PC).

[0037] The running data storage unit 14 stores data on wind direction and speed on the race course C and data on the coordinates (positions) of the rounding marks (SG, a, b), and also stores data on the coordinates (positions) that change as each player (PS, PA, PB, PC) runs (moves), data on the date and time, and speed data obtained in accordance with the changes in the coordinates, in association with the player ID as running data for each player (see Figure 6).

[0038] The Leg extraction unit 15 extracts multiple straight sections (Legs) of the course traveled by each player (PS, PA, PB, PC) based on changes in coordinates (positions) associated with the player's running (movement).

[0039] The R / D / U judgment value input unit 16 inputs a judgment value for each type (R / D / U) of direction of travel (R (sideways) / D (downwards) / U (upwards)) for determining which type of leg the leg belongs to, based on the speed (VMG) of the leg (which may be calculated based on running data), for each leg of each athlete extracted by the leg extraction unit 15.

[0040] The type determination unit 17 determines which type (R (sideways) / D (downwards) / U (upwards)) each of the multiple legs of each player belongs to, based on the legs and their speeds (VMG) of each player extracted by the leg extraction unit 15 and the judgment values ​​for each type (R / D / U) input by the R / D / U judgment value input unit 16.

[0041] The Leg data storage unit 18 stores the Leg data for each type (R / D / U) of the reference player PS and the comparison players PA, PB, and PC determined by the type determination unit 17, in association with the direction (P / S) and Start-Finish Time (start time, end time) of the Leg (see Figure 7).

[0042] In this embodiment, the Leg data is stored as candidate indices [candidate evaluation values] (evaluation items) for evaluating the PA, PB, and PC of a comparison player against the reference player PS, with the Max Speed ​​(maximum speed), 10s AVE (maximum speed averaged over 10 seconds), AVE (average speed for the section), and VMG (average speed for the section in the wind direction: "+" when up, "-" when down) corresponding to the Leg.

[0043] The similarity estimation unit 19 estimates the similarity of the legs of each of the comparison players PA, PB, and PC for each leg type (R / D / U) of the reference player PS, and creates an evaluation value comparison list L (see Figure 11) in which the leg data of the legs with high similarity is listed in correspondence with the leg data of the reference player PS for each leg type (R / D / U).

[0044] The similarity of the legs increases as the legs are in the same direction and distance and the start times of the legs are closer, with the maximum being "1."

[0045] In this embodiment, the similarity R between the legs of the comparison players PA, PB, and PC and the leg of the reference player PS is calculated using the following formula (E) (see Figure 8). Note that the description of the formula in the specification is written in a different way from the natural mathematical notation for convenience due to the character input function. The correct natural mathematical notation is as shown in Figure 8. Formula (E): Similarity R = Ratio of the comparison player's leg vector "Leg (→ b)" to the reference player's leg vector "Leg (→ a)" - α Start Time Difference (minutes) *The coefficient α is the proportion that the difference in leg start time (= environmental change) affects similarity R; for example, if similarity R decreases by 10% in one minute, α = 0.1.

[0046] If the angle between the reference player's Leg vector "Leg(→a)" and the comparison player's Leg vector "Leg(→b)" is "θ", the ratio of "Leg(→b)" to "Leg(→a)" is calculated using the following formula: 1) |→b|·cos θ / |→a| (when R≦1) 2-|→b|·cos θ / |→a| (when R>1) 2) |→b|·cos θ / |→a| (when |→a|≧|→b|) (|→a| / |→b|)·cos θ (when |→a|<|→b|)

[0047] In the comparison unit 20, the evaluation value difference calculation unit 21 compares, for each evaluation value included in the Leg data for each Leg of the reference player PS, the evaluation value included in the Leg data for each of the comparison players PA, PB, and PC, with the evaluation value included in the Leg data for the same Leg of the reference player PS, and adds the difference data to the evaluation value comparison list L (see FIG. 12 ). Here, for the evaluation values ​​(10s AVE) included in the Leg data for each of Legs (1) to (6), differences d1 to d6 (speed differences in the 10-second average maximum speed) between the reference player PS and the comparison players PA, PB, and PC are calculated and added to the evaluation value comparison list L.

[0048] The comparison leg data storage unit 22 stores an evaluation value comparison list L created by the similarity estimation unit 19 and to which the evaluation value difference calculation unit 21 adds the evaluation value differences d1 to d6 of each of the comparison players PA, PB, and PC relative to the reference player PS.

[0049] The evaluation value calculation unit 23 calculates the average value AVE(X) of the evaluation value differences (here, the speed difference of the 10-second average maximum speed) calculated by the evaluation value difference calculation unit 21 for each leg of the direction of travel (R / D / U) for each of the comparison athletes PA to PD in the evaluation value comparison list L created by conducting multiple simulated races (Practice 1, 2, 3, ...) to be compared (see Figure 13 (A)).

[0050] The ability judgment value input unit 24 inputs an ability judgment value for judging, for example, a four-level ability value (◎ / ○ / △ / ×) in comparison with the evaluation value of the reference player PS, based on the average value AVE(X) of the differences in the evaluation values ​​of each of the comparison players PA to PD calculated by the evaluation value difference calculation unit 21 (see Figure 13 (B)).

[0051] The ability value creation unit 25 creates an ability value (◎ / ○ / △ / ×) for each leg of the direction of travel (R / D / U) based on the average value AVE(X) of the differences in the evaluation values ​​of each of the comparison players PA to PD calculated by the evaluation value difference calculation unit 21, based on the ability judgment value input by the ability judgment value input unit 24 (see Figure 13 (C)).

[0052] In the presentation unit 30, the ability value presentation unit 31 presents the ability values ​​(◎ / ○ / △ / ×) for each leg of the direction of travel (R / D / U) of each of the comparison players PA to PD created by the ability value creation unit 25 to the comparison players PA to PD, for example, as the result of an ability comparison.

[0053] FIG. 4 is a block diagram showing the hardware configuration of the driving performance comparison device 1.

[0054] As shown in FIG. 4, the driving performance comparison device 1 includes a control circuit (processor) 41 which is a control unit.

[0055] The control circuit 41 is connected via a system and data bus to a memory 42 such as a flash ROM, a hard disk drive 43 including a storage medium 43a such as a magnetic disk, a user interface 44 including keys, switches, external input terminals for user operation from the outside, and a display, speaker, printer, and external output terminals for outputting data, a wired communication module 45 and / or a wireless communication module 46 for communicating with the outside, and the like.

[0056] The wind direction and speed measuring device 50 attached to the motor board BC, the mark coordinate measuring device attached to the rounding marks (SG, a, b), and the player coordinate measuring devices 60S, 60A, 60B, ... attached to the boards of each player PS, PA, PB, ... may be connected to the running ability comparison device 1 via a wired communication module 45 and / or a wireless communication module 46, for example, via a communication network such as the Internet.

[0057] The control circuit 41 controls the operation of each part of the driving performance comparison device 1 based on the control information stored in or read from the control information area 42b of the memory 42, in accordance with a control program that controls the processing of the driving performance comparison device 1 and that is stored in the control program area 42a of the memory 42. The control circuit (processor) 41 is not limited to one processor, and may include multiple processors.

[0058] The control program stored in the control program area 42a of the driving ability comparison device 1 includes functions corresponding to the processes performed by the skill capture / analysis unit 10, the comparison unit 20, and the presentation unit 30 described with reference to Figure 3.

[0059] In addition, the management information area 42b of the driving capability comparison device 1 also functions as the driving data memory unit 14, Leg data memory unit 18, and comparison Leg data memory unit 22 described with reference to Figure 3, and stores or reads out data that is input, acquired, generated, updated, output, etc. in accordance with each process performed by the driving data acquisition unit 11, rounding mark coordinate input unit 12, date and time input unit 13, Leg extraction unit 15, R / D / U judgment value input unit 16, type judgment unit 17, similarity estimation unit 19, evaluation value difference calculation unit 21, evaluation value aggregation unit 23, capability judgment value input unit 24, capability value creation unit 25, and capability value presentation unit 31.

[0060] In the driving performance comparison device 1 configured in this manner, the control circuit (control unit) 41 controls the operation of each part in accordance with the instructions written in the control program, and the software and hardware work together to realize various functions as described in the operation explanation below.

[0061] (Operation of the embodiment) Next, the operation of the driving performance comparison device 1 using the Leg similarity of the embodiment will be described.

[0062] FIG. 5 is a flowchart showing an example of a driving performance comparison process performed by the driving performance comparison device 1.

[0063] In step S1, the running data acquisition unit 11 acquires data on wind direction and speed on the race course C from the wind direction and speed measuring device 50 attached to the motor board BC, and also acquires coordinate data associated with the running of the reference player PS and comparison players PA, PB, ... from the player coordinate measuring devices 60S, 60A, 60B, ... attached to the boards of each player PS, PA, PB, ..., and stores this data in the running data memory unit 14 as running data.

[0064] FIG. 6 is a diagram showing the relationship between a running data acquisition screen G displayed on the display when the running performance comparison device 1 acquires running data and the running data stored in the running data storage unit 14.

[0065] The running data memory unit 14 stores coordinate data that changes as each player (PS, PA, PB, ...) runs, date and time data input by the date and time input unit 13, and speed data obtained in response to the change in coordinates, all in correspondence with the player ID.

[0066] In step S2, the Leg extraction unit 15 extracts, for each athlete (PS, PA, PB, ...), multiple straight sections (Legs) from the course traveled by that athlete based on the changes in coordinates (positions) associated with the athlete's running, which are stored in the running data storage unit 14.

[0067] In step S3, the type determination unit 17 determines the type (R (sideways) / D (downwards) / U (upwards)) of each of the multiple legs of each player based on the legs and their speeds V (VMG) of each player extracted by the leg extraction unit 15 and the judgment values ​​for each type (R / D / U) input by the R / D / U judgment value input unit 16.

[0068] For example, if the leg speed V for each player is "-10<V<+10", it is determined to be the direction of travel type (R); if "V≦-10", it is determined to be the direction of travel type (D); and if "V≧+10", it is determined to be the direction of travel type (U).

[0069] Then, the type determination unit 17 stores the leg data for each type (R / D / U) of the determined reference player PS and the comparison players PA, PB, etc. in the leg data storage unit 18, corresponding to the leg direction (P / S) and start-finish time (start time, end time), as shown in Figure 7.

[0070] FIG. 7 is a diagram showing an example of the Leg data for each type (R / D / U) of the reference player PS and the comparative players PA, PB, . . . stored in the Leg data storage unit 18. In FIG.

[0071] Specifically, the Leg data is stored as Max Speed ​​(maximum speed), 10s AVE (maximum speed averaged over 10 seconds), AVE (average speed over the section), and VMG (average speed over the section in the wind direction: "+" when up, "-" when down) corresponding to the Leg as candidate indices [candidate evaluation values] for evaluating the comparison players PA, PB, etc. against the reference player PS.

[0072] In step S4, the similarity estimation unit 19 creates a leg vector for each leg based on the leg coordinates extracted by the leg extraction unit 15, corresponding to the start time-end time of the leg data for each type (R / D / U) of the reference player PS and the comparison players PA, PB, ... stored in the leg data storage unit 18.

[0073] Here, the Leg vector of the reference player is "Leg (→a)" and the Leg vector of the comparison player is "Leg (→b)".

[0074] In step S5, the similarity estimation unit 19 calculates (estimates) the similarity R of each leg of the comparison players PA, PB, ... to the leg of the reference player PS for each type (R / D / U) in accordance with the calculation formula (E) shown in Figure 8, which was also explained above.

[0075] FIG. 8 is a diagram illustrating a calculation formula for the similarity between the Leg vector "Leg (→a)" of the reference player PS and the Leg vector "Leg (→b)" of the comparison players PA, PB, . . .

[0076] FIG. 9 is a diagram showing an example (Case 1) of calculating the similarity R between the leg vector PS2 (Leg) of the reference player PS and the leg vectors PA2 (Leg 1) / PA3 (Leg 2) / PA4 (Leg 3) of the comparison player PA.

[0077] Here (Case 1), for course PS2 (Leg) of reference athlete PS who passed rounding mark a on the downhill side, the similarity R with course PA2 (Leg 1) of comparison athlete PA, which has the same start time and direction but a slightly shorter distance, is 0.8; the similarity R with course PA3 (Leg 2) of comparison athlete PA, which has a start time 40 seconds later, a direction that is approximately 90° different, and a slightly shorter distance, is approximately 0; and the similarity R with course PA4 (Leg 3) of comparison athlete PA, which has a start time 1 minute 40 seconds later, approximately the same direction, and a significantly shorter distance, is 0.04, meaning that the similarity R with course PA2 (Leg 1) of comparison athlete PA is the highest.

[0078] FIG. 10 is a diagram showing an example (Case 2) of calculating the similarity R between the leg vector PS2 (Leg) of the reference player PS and the leg vectors PA2 (Leg 1) / PA3 (Leg 2) / PA4 (Leg 3) of the comparison player PA.

[0079] Here (Case 2), for course PS2 (Leg) of reference athlete PS who passed rounding mark a on the downhill, the similarity R with course PA2 (Leg 1) of comparison athlete PA, which has the same start time and direction but a much shorter distance, is 0.2; the similarity R with course PA3 (Leg 2) of comparison athlete PA, which has a start time 12 seconds later, a direction that is approximately 90° different, and a slightly shorter distance, is approximately 0; and the similarity R with course PA4 (Leg 3) of comparison athlete PA, which has a start time 1 minute later, approximately the same direction, and a slightly shorter distance, is 0.7, meaning that the similarity R with course PA4 (Leg 3) of comparison athlete PA is the highest.

[0080] FIG. 11 is a diagram showing an example of the evaluation value comparison list L created by the similarity estimation unit 19. As shown in FIG.

[0081] In step S6, the similarity estimation unit 19 creates an evaluation value comparison list L in which the leg data of the legs with high similarity are listed in association with the leg data of the reference player PS for each type (R / D / U) based on the similarity of the legs through which each of the comparison players PA, PB, and PC passed, calculated (estimated) in step S5, for the legs for each type (R / D / U) of the reference player PS, as shown in Fig. 7, for example. The similarity estimation unit 19 stores the created evaluation value comparison list L in the comparison leg data storage unit 22.

[0082] In step S7, the evaluation value difference calculation unit 21 targets the evaluation value comparison list L stored in the comparison leg data storage unit 22, compares each evaluation value contained in the leg data for each leg of the reference player PS with the evaluation value contained in the leg data for the same leg of each of the comparison players PA, PB, and PC, and adds the difference data to the evaluation value comparison list L.

[0083] Figure 12 shows an evaluation value comparison list L created by the evaluation value difference calculation unit 21 by adding, for each evaluation value contained in the leg data for each leg of the reference player PS, the difference in the evaluation values ​​contained in the leg data for the same leg of each of the comparison players PA, PB, and PC.

[0084] Specifically, for example, as shown by the box W1 in Figure 12, for each evaluation value (10s AVE) included in the Leg data for each Leg (1) to (6), the differences d1 to d6 between the reference player PS and the comparison players PA, PB, and PC are calculated and added to the evaluation value comparison list L.

[0085] Figure 13 is a diagram illustrating the relationship between the average value AVE(X) of the differences in the evaluation values ​​of each of the comparison players PA to PD compiled by the evaluation value compilation unit 23 and the ability values ​​(◎ / ○ / △ / ×) for each leg of the direction of travel (R / D / U) of each of the comparison players PA to PD created by the ability value creation unit 25.

[0086] In step S8, the evaluation value tallying unit 23 tally up the differences in evaluation values ​​(here, the speed differences of the 10-second average maximum speed) for each Leg (Legs (1) (2) (4)) of the direction of travel (R / D / U) for each of the comparison athletes PA to PD in the evaluation value comparison list L created by conducting multiple simulated races (November 15th / 16th / 20th / 21st), as shown in FIG. 13(A), for example, and calculates the average value AVE(X) as shown by the frames W2 and W3 in FIG. 13(A).

[0087] In step S9, the ability value creation unit 25 uses the average value AVE(X) of the differences in the evaluation values ​​of each of the comparison players PA to PD calculated by the evaluation value difference calculation unit 21 as the target and creates an ability value (◎ / ○ / △ / ×) for each leg of the direction of travel (R / D / U) as shown in Figure 13(C) based on the ability judgment value input by the ability judgment value input unit 24 as shown in Figure 13(B).

[0088] Specifically, if the average value AVE(X) of the differences between the evaluation value of the reference player PS and the evaluation values ​​of the comparison players PA to PD is greater than "0", then "X>0" is judged as an ability value ◎ (good); if it is less than "0" and greater than "-1", then "-1<X≦0" is judged as an ability value ○ (not inferior); if it is less than "-1" and greater than "-3", then "-3<X≦-1" is judged as an ability value △ (slightly inferior); and if it is less than "-3", then "X≦-3" is judged as an ability value × (inferior).

[0089] In step S9, the ability value presentation unit 31 presents (outputs) to the comparison players PA to PD the ability values ​​(◎ / ○ / △ / ×) for each leg (leg (1) (2) (4)) of the type of direction of travel (R / D / U) of each of the comparison players PA to PD, which have been created as shown enclosed in frames W2 and W3 in Figures 13 (A) and 13 (C), as ability evaluation results.

[0090] The ability evaluation results presented (output) by the ability value presentation unit 31 for the comparison players PA to PD may be displayed on a display which is the user interface 44, or may be transmitted to the communication terminals (not shown) of the comparison players PA to PD via the wired communication module 45 and / or the wireless communication module 46 for presentation.

[0091] This allows, for example, setting a target standard athlete without having to secure personnel with analytical capabilities such as a sports coach or analyze actual running data, and automatically comparing the abilities of a comparison athlete (e.g., a competitive athlete) on a leg-by-leg basis (creating ability scores). By presenting this information, it becomes clear in a short time which points the comparison athlete can improve upon (for example, on race course C shown in Figure 1, the first downhill leg (Leg (2)) from the reaching (Leg (1)) was given an ability score of △, and the speed thereafter (Leg (3) (4)) was comparable and gave an ability score of 〇, but the latter half of the warm-up leg (Leg (5)) was not able to catch the wind well and therefore gave an ability score of △).

[0092] In other words, by automating this method of comparing running ability, it becomes possible to provide efficient feedback in a short amount of time on the points that athletes should be aware of in order to improve, thereby improving the quality of training aimed at improvement.

[0093] (Summary of the embodiment) According to the running ability comparison device 1 using Leg similarity of the embodiment, in windsurfing, where athletes change their course on the race course C depending on changes in the weather environment such as wind and waves, the wind direction on the race course C is measured, and running data including the coordinates (positions) and date and time associated with the running of each of the preset reference athlete PS and comparison athlete PA is obtained, and the straight sections (Leg) that top-level athletes consider important are extracted.

[0094] Then, for each leg of the reference player PS for each type of direction of travel (R / D / U), the similarity R between the leg of the comparison player PA and that of the reference player PS is calculated (estimated) based on the direction, distance, and start time of each leg, and the ability of the comparison player PA is evaluated based on the difference between the evaluation value (for example, 10s AVE (average maximum speed over 10 seconds)) of the reference player PS of the leg with the highest similarity and the evaluation value of the comparison player PA.

[0095] In the embodiment, the difference in evaluation value between the reference player PS and the comparison player PA for each type (R / D / U) in any one simulated race is used as the primary evaluation value, and the average value of the difference in evaluation values ​​for any multiple times is used as the secondary evaluation value.

[0096] Therefore, it becomes possible to compare the abilities of top-level athletes based on the straight section (Leg), which is important to them, without restricting the athletes' ability to perform to their full potential by, for example, fixing the athletes' course on race course C.

[0097] Although the running ability comparison device 1 of the embodiment has been described as a method for comparing legs in windsurfing, the comparison method of the embodiment may be applied to any race that includes multiple legs (straight lines) where the reference and comparison subject, which are the premise of the comparison method, run side by side and running data can be obtained, and where each athlete's course selection is strategic. For example, the device may also be applied to improvement training in yachting and surfing competitions, where the environment on the sea, such as wind and waves, is constantly changing, as in windsurfing, in skiing competitions, where the environment on land, such as weather and snow surface, is constantly changing, and in motorsports competitions, where the weather and road surface are constantly changing.

[0098] Furthermore, even if the reference athlete PS and the comparison athlete PA do not run side by side, by using one's own best running data (personal best) as the reference athlete and using other running data from the same day with little environmental change (when the coefficient α is small) as the comparison athlete, it becomes possible to compare personal bests on a leg-by-leg basis, which may also be applied to self-improvement training.

[0099] The present invention is not limited to the above-described embodiments, and various modifications can be made in the implementation stage without departing from the spirit of the invention. Furthermore, the embodiments may be implemented in appropriate combinations, in which case the combined effects can be obtained. Furthermore, the above-described embodiments include various inventions, and various inventions can be extracted by combining selected elements from the disclosed elements. For example, if the problem can be solved and the desired effect can be obtained even if some elements are deleted from all elements shown in the embodiments, the configuration from which these elements are deleted can be extracted as an invention.

[0100] DESCRIPTION OF SYMBOLS 1 ... Running ability comparison device using Leg similarity SG ... Start goal mark a ... Rounding (upper) mark b ... Rounding (lower) mark C ... Race course PS ... Reference athlete PA, PB, PC ... Comparison athlete Leg ... Straight portion of course selection 11 ... Running data acquisition unit 12 ... Rounding mark coordinate input unit 13 ... Date and time input unit 14 ... Running data storage unit 15 ... Leg extraction unit 16 ... R / D / U judgment value input unit 17 ... Type judgment unit 18 ... Leg data storage unit 19 ... Similarity estimation unit L ... Evaluation value comparison list 21 ... Evaluation value difference calculation unit 22 ... Comparison Leg data storage unit 23 ... Evaluation value aggregation unit 24 ... Ability judgment value input unit 25 ... Ability value creation unit 31 ... Ability value presentation unit 41 ... Control circuit (control unit) 42a ... Control program area 42b ... Management information area 43 ... Disk drive 44 ... User interface 45... Wired communication module 46... Wireless communication module 50... Wind direction and speed measuring device 60S, 60A, 60B, 60C... Player coordinate measuring device

Claims

1. A running ability comparison method using leg similarity in which a control unit of a running ability comparison device performs the following processes: acquires running data, including coordinates and times, associated with the running of each of a reference athlete and a comparison athlete, on a course that includes types of running direction that correspond to wind direction; extracts the legs run by the reference athlete and the comparison athlete based on the running data; calculates the similarity between the extracted legs of the reference athlete and the comparison athlete for each type of running direction; calculates the difference between an evaluation value obtained based on the running data of the leg of the reference athlete that has the highest similarity and an evaluation value obtained based on the running data of the leg of the comparison athlete; and outputs the difference in evaluation values ​​as the evaluation result of the comparison athlete.

2. The running ability comparison method according to claim 1, wherein the similarity between the leg of the reference runner and the leg of the comparison runner is calculated based on the direction, distance, and start time of each leg.

3. A running ability comparison method as described in claim 1 or claim 2, wherein the control unit of the running ability comparison device calculates an average value of the differences in the evaluation values ​​based on multiple runs of the reference athlete and the comparison athlete on the course, and outputs the average value of the differences in the evaluation values ​​as the evaluation result of the comparison athlete.

4. A program that causes the control unit of a running ability comparison device to function to execute the following processes: acquire running data including coordinates and times associated with the running of each of a reference athlete and a comparison athlete on a course including types of traveling direction according to wind direction; extract the legs run by each of the reference athlete and the comparison athlete based on the running data; calculate the similarity between the extracted legs of the reference athlete and the comparison athlete for each type of traveling direction; calculate the difference between the evaluation value obtained based on the running data of the leg of the reference athlete that has the highest similarity and the evaluation value obtained based on the running data of the leg of the comparison athlete; and output the difference in evaluation values ​​as the evaluation result of the comparison athlete.

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