JP5412531B2 - Athletic ability evaluation system and computer storage medium - Google Patents

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application is filed on Feb. 2, 2009, US Provisional Patent Application No. 61 / 148,293 entitled “Athletic Capability Evaluation System” filed on Jan. 29, 2009. US Patent Application No. 61 / 149,251 entitled “Athletic Ability Evaluation System” (Attorney Docket No. NIKE.146269) and “Athletic Ability Evaluation System” filed on September 14, 2009; Claims the benefit of the entitled US patent application Ser. No. 12 / 559,082 (Attorney Docket Number: NIKE.146275).

  This application is related in content to US patent application Ser. No. 61 / 169,993 (Attorney Docket No .: NIKE.146269) entitled “Athletic Capability Evaluation System” filed April 16, 2009 and May 5, 2009. Related to US Patent Application No. 61 / 174,853 (Attorney Docket: NIKE.148870) entitled “Athletic Capability System Evaluation System” filed on Jan. 1.

  The present invention relates to athletic ability evaluation and related ability measurement systems, and mainly relates to athletic activities such as training and athlete evaluation.

  Athletics is very important in society. In addition to competing with each other on the field, athletes often compete with each other on the field. For example, student athletes compete against each other for joining a team, longer play time or higher starting order. Students in the final year who graduate from high school are in a competitive relationship over sports scholarships, etc. at universities where everyone aspires. In addition, amateur athletes in specific sports compete with each other for jobs that are professional athletes in specific sports. An important factor in all these competitions is the athletic ability or athletic ability of the particular athlete in question, and the ability to show this to others or record it in text.

  Speed, agility, reaction time, power, etc. are some of the characteristics that determine an athlete's athleticism. Thus, athletes strive to improve athletic performance in these areas, and coaches and recruiters tend to choose athletes with the best combination of these characteristics for a particular sport.

  To date, athlete evaluation and comparison has been largely subjective. Scouts circulate around the country in search of potential athletes to be hired for a particular team, and many top athletes are hired through hearings without being seen in the field. Such athlete evaluation and adoption methods usually have one or eight properties.

  One way to evaluate and compare an athlete's athletic is to have the athlete perform a common combination of exercise and drill. Athletes that can exercise or drill faster and / or more accurately are usually considered better than those who do the exercise or drill more slowly or less accurately. For example, so-called “cone drills” are routinely used to train and evaluate athletes. In a typical cone drill, the athlete follows a predetermined course between multiple marker cones, and in the process, makes several sharp turns and / or from forward to reverse or sideways. Make a conversion.

  Although used in many institutions (eg, high schools, universities, training camps, and amateur and professional teams), drills for such training and testing are a subjective assessment of coaches or trainers. Or it relies on a time measuring device that is manually triggered by a human operator. Therefore, these methods are essentially under the influence of human senses and errors. Such deviations and errors due to human sensations can also prevent the best athletes from being found or rewarded.

  Further, efforts to meaningfully aggregate and evaluate information related to the timing and the like collected in these exercises and drills have been limited so far. For example, a given drill can determine the fastest athlete in a population, but these known systems may not have participated in the exact same drill on the same day. It does not allow a meaningful comparison with athletes from around the world.

  For example, in basketball, university and high school level athletes are determined for their ability to play an active role in the National Basketball League (NBA) based at least on their performance in NBA sponsored pre-draft camps. In this camp, athletes are trained to represent each player's abilities in order to allow each NBA franchise to make an informed decision at the draft date that the player is selected. Take the test.

  While such tests provide each NBA franchise with a snapshot of the capabilities of a given player for a particular test, any of these tests can provide a comprehensive athletic assessment and / or ranking. It was not compiled. These tests are merely discrete data points that are referred to in the metaphorical vacuum space and do not consider the results of each test in relation to other tests. Furthermore, such test scores do not bring much benefit to the promising university level, high school level and youth athletes. Because the pre-draft test cannot be easily extended, it is necessary for college, high school and young athletes to determine their ability and compare it to that of future or active NBA players. It is not possible.

  Embodiments of the invention relate to a method for assessing athlete performance. In one embodiment, the present invention is directed to an athletic evaluation method for normalizing and more accurately comparing the overall performance of at least two athletes. Each athlete takes at least two different athletic performance tests. Each test is designed to measure the different athletic skills required to compete efficiently in a defined event. The results from each test for an athlete are normalized against a database that provides a distribution of test results from a group of similar athletes, and point values based on the percentile of the test results in the distribution of test results are normalized. Assign to each test result. By combining the point values derived from the results from the at least two different athletic performance tests, an athletic assessment score is generated that represents the overall athletics of each athlete.

  In the case where the defined event is basketball, athletic performance tests include, for example, measuring an athlete's no-step vertical jump height, measuring an athlete's approach jump reach height, an athlete's sprint time for a given distance, And a measurement of the lap time for a given course. The method further includes matching the height of the no-step vertical jump, the reach height of the approach jump, the sprint time and the lap time to at least one quick reference table for synthesizing an athletic evaluation score. Can do. A magnification factor can be applied to the calculated athletic evaluation score so that the evaluation score of the group of tested athletes falls within a desired range.

  This summary is provided to introduce a selection of concepts further described in the detailed description that follows. This summary is not intended to identify key features or essential features of a claim, nor is it intended to be used to help define the scope of a claim.

  The present invention is described in detail below with reference to the accompanying drawings.

FIG. 1 is a flowchart of an athletic evaluation system based on the principle of the present invention. FIG. 2 is a data collection card user interface for use with the athletic evaluation method of FIG. FIG. 3 is a schematic diagram of a test facility for use in the testing facility and the athletic evaluation system of FIG. FIG. 4 is a perspective view of an athlete performing a no-step vertical jump test in accordance with the principles of the present invention. FIG. 5 is a perspective view of a test apparatus for determining the maximum touch height according to the principle of the present invention. FIG. 6 is a perspective view of the test apparatus of FIG. 5 showing an athlete performing the highest touch test in accordance with the principle of the present invention. FIG. 7 is a schematic diagram of a test configuration for determining lane agility in accordance with the principles of the present invention. FIG. 8 is a perspective view of an athlete performing two-handed throwing using a medical ball for determining a kneeling (knee contact state) power vault toss according to the principle of the present invention. FIG. 9 is a perspective view of an athlete performing a multi-stage hurdle test in accordance with the principle of the present invention. FIG. 10 is a performance chart based on the principle of the present invention. FIG. 11 is a table showing an example of data collected in a test event related to basketball. FIG. 12 is an exemplary chart for a female athlete for a no-step vertical jump for basketball. FIG. 13 is an exemplary graph showing data in the practice of a no-step vertical jump for some female athletes tested for basketball. FIG. 14 is a table showing the “w-score” of an exemplary female athlete applicable to basketball. FIG. 15 is a table showing a “w score” for an exemplary female athlete applicable to basketball. FIG. 16 is a flow diagram illustrating a method for synthesizing an exemplary athletic assessment score in accordance with an embodiment of the present invention. FIG. 17 is a block diagram of an exemplary computing environment suitable for implementing embodiments of the present invention. FIG. 18 is another embodiment of an exemplary performance chart for use in synthesizing athletic ratings for fast pitch softballs in accordance with the principles of the present invention. FIG. 19 is a table showing an example of data collected in the test event for the first pitch softball. FIG. 20 is an exemplary chart for a female athlete's vertical jump for a fast pitch softball. FIG. 21 is an exemplary graph showing data relating to vertical jumps in the field of some female athletes tested on a fast pitch softball. FIG. 22 is a table showing an exemplary female athlete “w score” applicable to a fast pitch softball. FIG. 23 is a table showing exemplary female athlete “w-scores” applicable to a fast pitch softball.

  In order to satisfy legal requirements, the content of the present invention is described in detail herein with specificity. However, the description is not intended to limit the scope of patents arising from this application. Rather, the inventor also envisions that the claimed subject matter may be practiced in other ways, and may include steps that are different or similar or different from steps described herein. Yes, and can be accompanied by other current technologies or future technologies. In addition, the terms “step” and / or “block” are used to refer to different parts of the method used, but the specifics between the individual steps in this disclosure are not explicitly stated unless the order is explicitly specified. Must not be construed as having been specified.

  Embodiments of the invention relate to a method for assessing athlete performance. In one embodiment, the present invention is directed to an athletic evaluation method for normalizing and more accurately comparing the overall performance of at least two athletes. Each athlete completes at least two different athletic performance tests. Each test is designed to measure the different athletic skills required to compete efficiently in a defined event. The results from each test for an athlete are normalized against a database that provides a distribution of test results from a group of similar athletes, and point values based on the percentile of the test results in the distribution of test results are normalized. Assign to each test result. By combining the ranking numbers derived from the results from the at least two different athletic performance tests, an athletic evaluation score representing the overall athletic performance of each athlete is generated.

  With particular reference to FIG. 1, a method 10 for assessing athletics is provided, which is designed to assess the athletic ability and / or performance of a given athlete by synthesizing an overall athletic assessment score for the athlete. Performing at least two different athletic tests.

  Each test is designed to measure the different athletic skills required to compete efficiently in a defined event. For example, in the basketball event, the athletic assessment method 10 involves performing four separate tests, which can be used to determine the overall athletic assessment of a male athlete. In another configuration, the athletic assessment method 10 involves performing six individual tests, which can be used to determine the overall athletic assessment of a female athlete in the basketball event. An exemplary test facility and configuration is schematically illustrated in FIG. Test facilities and equipment used to measure and collect test data are disclosed in US patent application Ser. No. 11 / 269,161 filed Nov. 7, 2005, in common with the present holder. The disclosure is incorporated by reference in its entirety.

  Continuing with reference to FIG. 1, a test process for determining the athlete's overall athletics is initialized at step 12 and at step 14 it is determined whether the test athlete is male or female. If the test athlete is a male, the athlete's weight can be measured in step 16 and recorded on a data collection card as illustrated in FIG. After the weight measurement, in step 18, the athlete performs a no-step vertical jump.

  The no-step vertical jump generally represents the athlete's lower body peak power development and is performed on the coat surface or other hard planar horizontal surface. In order to achieve maximum height, athletes perform a recoiled vertical jump by jumping with both feet after dropping their posture with a squat, using arm swinging (see Figure 4). Vertical jump measurements can be recorded on a physical or electronic data collection card (see FIG. 2).

  After the athlete's weight and the height of the no-step vertical jump are recorded on the data collection card, the athlete's peak power can be calculated in step 20. The calculated peak power can be displayed and recorded on the data collection card along with the weight and height of the no-step vertical jump.

  As described above, the no-step vertical jump measures the ability of the athlete to jump in the vertical direction from substantially the standing position. In addition to determining the height of a no-step vertical jump (i.e., jump from a position that is largely unmoved), the athletic evaluation method 10 also includes measuring an approach jump, which is an athlete's functional In order to assess the ability to jump, athletes are allowed to run or walk towards the target.

  As shown in FIGS. 5 and 6, for example, a scale such as a tape measure can be fixed to a stationary object such as a backboard. Once the scale is fixed on the backboard, athletes are allowed to approach the scale from approximately 15 feet radius to determine the highest point of reach from the floor, and jump with one or both feet. In this case, one arm is extended toward the scale. When an athlete takes an approach and jumps from the floor, the approach jump reach height can be read visually or by an electronic sensor, relative to the athlete's hand relative to the scale. At 22, it can be recorded as the athlete's “best touch”. As with peak power, the highest touch can be recorded in the data collection card of FIG.

  After measuring the reach height of the approach jump, the athlete can be sprinted over a predetermined distance with time measurement. In one configuration, the athlete sprints over a distance of approximately 75 feet, which is roughly a distance of approximately three quarters of the total length of the basketball court. In step 24, the time it takes for the athlete to sprint is measured and can be recorded on the data collection card of FIG.

  Referring to FIG. 7, an athlete's agility can be determined by measuring the time it takes for the athlete to follow a predetermined course. In one configuration, the course is essentially a 16 foot by 19 foot rectangle, which is roughly the same size as the so-called “paint” or “box” area of the basketball court. By measuring the time it takes for an athlete to cross the paint section, the overall agility of the athlete can be determined. The athlete can be instructed to make one or more rounds around the box. In step 26, the time it takes for the athlete to lap can be measured and recorded on the data collection card.

  In addition to the above peak power, maximum touch, three-quarters court sprint and lane agility, male athletes may be required to perform kneeling power vaults in step 28 and in multi-step in step 30. You may be required to perform a hurdle test. FIG. 8 provides an illustration of test equipment that an athlete can use to throw a therapeutic ball to determine a kneeling power vault toss rating. Specifically, the athlete throws a predetermined weight of therapeutic ball from the kneeling position. In one configuration, the therapeutic ball is 3 kilograms and is typically thrown with both hands by an athlete from a kneeling position. The total flight distance of the therapeutic ball can be measured and recorded on a data collection card.

  As shown in FIG. 9, the multi-stage hurdle test is performed by repeatedly jumping to an athlete for a predetermined time (interval) on one hurdle. In one configuration, the number of athletes jumping on both legs through a 12-inch high hurdle over two 20-second intervals is recorded, and these intervals may be separated by a single 10-second break interval. it can. On the data collection card, it is possible to record the number of successful jumps on both legs as multi-level hurdle evaluation.

  Although male athletes can require kneeling power vaults and multi-step hurdle tests, these data are useful and may prove the athlete's overall athletic ability, It may not be used to determine overall athletic assessment.

  Results from each test for an athlete are normalized relative to a database that provides a distribution of test results from a similar group of athletes, and the percentiles in the normal distribution of the test results in the distribution of test results are normalized. Assign a ranking number based on each test result. For example, peak power, best touch, 3/4 coat sprint and lane agility data correspond to a single table or peak power, best touch, 3/4 coat sprint and lane agility, respectively, at step 32. You can refer to the individual chart. The look-up table can include point values assigned based on scores for certain tests (ie, peak power, best touch, 3/4 coat sprint and lane agility). In step 34, the assigned point value can be recorded. In step 36, the scale values can be applied to the point values assigned by the quick reference and combined in step 38 for use in the synthesis of the overall athletic assessment. This process is further described in relation to FIG.

  Still referring to FIG. 1, if it is determined in step 14 that the test athlete is female, the height of the no-step vertical jump is recorded in step 40. As with male athletes, the no-step vertical jump test generally represents the athlete's lower body peak power development and is performed on the court surface or other hard flat horizontal surface. In order to achieve maximum height, athletes perform a recoiled vertical jump by jumping with both feet after dropping their posture with a squat, using arm swinging (see Figure 4).

  Following the height of the no-step vertical jump, the female athlete's highest touch is measured at step 42 and a three-quarter coat sprint is measured at step 44. Lane agility is measured at step 46 and used with no-step vertical jump height, maximum touch and 3/4 coat sprints to determine the female athlete's overall athletic assessment.

  As with male athletes, in step 48, the female athlete performs a kneeling power vault test and in step 50, a multi-stage hurdle test is performed. As shown in FIG. 8, although the test is performed on a female athlete in the same manner as a male athlete, the female athlete can use a lighter therapeutic ball. In one configuration, male athletes use 3 kilograms of therapeutic balls, while female athletes use 2 kilograms of therapeutic balls.

  After the above tests have been performed in steps 40, 42, 44, 46, 48 and 50, in step 52, no step vertical jump height, best touch, 3/4 coat sprint, lane agility, kneeling power Refer to vaults and multi-hurdle data in a single chart or individual chart.

  By referring to the data from each test, in step 54 the look-up table assigns a point value to each test. The points assigned in step 54 can be combined and scaled down later, so that a comprehensive athletic assessment can be synthesized in step 58 based on the points scaled up, scaled and combined.

  Tests conducted on female athletes are similar to those on males, but do not record the weight of female athletes. For this reason, peak power cannot be used in determining the overall athletic assessment of female athletes. Although peak power cannot be used in determining the overall athletic assessment of female athletes, reference should be made to the no-step vertical jump height, kneeling power vaults and multi-stage hurdles, as described above. Used to judge evaluation. An exemplary chart is shown in FIG. 10, which provides a performance assessment for each test in a series of tests for female athletes.

  Regardless of the athlete's gender, a quick reference can be determined by measuring and recording normative test data for athletes in the range of 100 to thousands. The normative data can be sorted by test to map performance ranges and establish percentile rankings and thresholds for each test value observed during athlete testing. Tabularized rankings can be scored and converted to points using statistical functions, thereby allowing each test (ie peak power, best touch, 3/4 coat sprint and lane agility) You can build a quick reference for each. Once the chart is constructed, the chart can be referenced for test data to determine the overall athletic assessment.

  Sample test data for one athlete can be obtained from the data collection card, ranked, scored, scaled down and scaled up to obtain a comprehensive athletic assessment.

  Data collected in the field regarding test events (eg, combine, camp, etc.) can be entered, for example, on a handheld device (not shown) and recorded in a database, on or from the handheld device. It can be displayed remotely in the format shown in FIG. Two trials can be allowed for each test. However, a multi-stage hurdle (MSH) is performed as one trial consisting of two jump stages.

  FIG. 11 shows an example of collected data. The test units for FIG. 11 are as follows: NSVJ = no-step vertical jump (unit is inch); Max Touch (highest touch) (unit is inch); MSH = multi-stage handle (multi-stage hurdle) (unit: number of jumps); Lane Agility (lane agility) (unit: seconds); three-quarter Court print (three-quarter coat sprint) (unit: seconds); KnPB = kneading Power Ball toss (Kneeling Power Vaults) (Unit: feet).

  The best result from each test is converted to an event point that includes a fractional part by matching the test result against a scoring table provided for each test. For male athletes' basketball assessment, no-step vertical jump is a test, but is an event scored by peak power (derived from weight and height of no-step vertical jump). In FIG. 12, a quick reference for no-step vertical jumps for female athletes (upper performance range) is provided to show an example of a quick reference. Each possible test result is associated with an event point that includes an assigned rank and fractional part.

  In the above example of FIG. 12, the rank assigned to each test result can be derived from normative data collected at several events across the country for hundreds of teen female basketball players. This normative data is sorted and each value is converted to a respective percentile by an empirical cumulative distribution function (eCDF). This percentile or rank is affected by raw test measurements (normative data) and depends on the size of the data set and the test value as a component.

  The athletic assessment scoring system described above includes two steps: normalizing the raw score and converting the normalized score to cumulative points. Normalization is necessary as a precondition for comparing data from different tests. Step 1 is to ensure that subsequent comparisons are meaningful, and Step 2 is a concrete nature of the scoring system (whether extremely high performance is gradually rewarded, or Decide whether the yield will decrease. The mapping developed in Step 2 converts the standardized score into points, so no update work is required, and it is valid for all tests regardless of the event and measurement range. By judicious selection of functions for normalization and transformation, it provides a consistent evaluation for evaluating performance according to predefined characteristics.

In order to compare the results of the different tests that make up a series of test batteries, it is necessary to standardize the results to a common range. If the data is normal, a well-known standardization is the z-score, which represents the (signed) number of standard deviations between the mean value and the measured value. However, if the data is non-normal, the z-score is no longer appropriate. This is because it does not provide a consistent interpretation of the different distributions. A more robust standardization method is the empirical cumulative distribution function (ECDF) u, which is defined as follows.

In the above equation, x is the raw measurement to be standardized; y ₁ , y ₂ ,. . . , Y _n are data used to calibrate the event, and II {A} is an indicator function that returns a value of 1 if event A occurs and returns a value of 0 in the other case. Note that u depends on both the raw measurement x of interest and the peers' raw measurement y.

The relationship with uε (0,1) is guaranteed in the sense of a strict inequality by 1/2 being added to the summation in square brackets and (n + 1) used in the denominator. Although this definition is cumbersome, u can be obtained by ordering and counting the combined data set containing all the calibration data (y ₁ , y ₂ ,..., Y _n ) and the raw data x to be standardized. It can be easily calculated.

That is,
u = {[number of y less than x] +0.5 [number of y equal to x] +1} / {[number of y] +1}
= {[Number of (y and x) less than x] +0.5 [number of (y and x) equal to x]} / [number of (y and x)]
It is.

  Note that this definition can also be applied to binned data (although raw data should be used where possible).

  Although the ECDF calculated in step 1 provides a common basis for comparing results from dissimilar tests, ECDF is not appropriate for assessing performance. This is because this approach is not consistently rewarded incrementally using the percentile “anchor” (sanity check). Therefore, it is necessary to convert the calculated percentile to an appropriate point scale (via a monotonous one-to-one mapping).

Such a conversion is provided by the inverse Weibull conversion and is given by

The function depends on two parameters (α and λ) and results in a scoring curve that is similar in nature to the two-parameter power law applied to the raw score. The parameters α and λ are selected so that the following four rules governing the relationship between the performance percentile and the given points are roughly satisfied.
1. The 10th percentile should achieve approximately 10% of the nominal maximum.
2. The 50th percentile should achieve approximately 30% of the nominal maximum.
3. The 97.7th percentile should achieve approximately 100% of the nominal maximum.
4). The 99.9th percentile should achieve approximately 125% of the nominal maximum.

  In general, since the four constraints cannot be satisfied by the two-parameter model at the same time, the parameters are selected so as to minimize a certain part of mismatch (in this case, the sum of squared log errors). However, the response of the estimation is relatively insensitive to the specific error metric to be selected.

  In describing the method for the case where raw (non-binned) data is obtained, three performances 12, 16, and 30 are used, and nine observations 16, 20, 25, 27, 19, 18, 26, Consider the case of scoring with a calibration data set containing 27 and 15.

For x = 16, there is one observation less than x and one observation equal to x in the calibration data (15). Therefore,

The summary of the calculation is shown in the table below.

To ensure backward compatibility, it may be necessary to score athletes based on binned data. Assume that the four performances 40, 120, 135 and 180 are scored with a calibration data set binned as follows. Here, the bin display represents the lower limit of the section. For example, the bin displayed as 90 includes measurement values belonging to the section (90, 100).

For x = 135, 0 + 2 +. . . There are + 17 + 26 = 219 observations stored in bins with less than x, and 14 stored in the same bin. Therefore,

And “bin containing 135” means a bin containing 135.

The summary of the calculation is shown in the following table.

  Standardization and conversion are done in exactly the same way as for raw data. However, care must be taken to ensure consistent handling of the bins. All raw values stored in the same bin yield the same standardized value, resulting in the same score. In short, scoring based on binned data simplifies data collection and recording at the expense of resolution (recording just intervals, not exact values) and complexity (consistent handling of bin displays). . In rare cases, only approximate statistics (eg, mean and standard) may be provided for calibration data. Assuming that the data is legitimate, the raw data can be standardized using the Microsoft® Excel® normdist () function.

  The rules described above rely heavily on the assumption of data standardity. Therefore, if the data does not have standardity, naturally, this method struggles. As a result of the assumed standardity, this approach does not enjoy the robustness of the ECDF method based on raw or binned data and should be avoided unless there is no other alternative.

  To illustrate this approach, assume that the mean and standard deviation of a normally distributed calibration data set is 98.48 and 24.71, respectively, and it is desirable to score x = 150. In this case, u = normdist ((150−98.48) /24.71) = 0.981.

As before,

It is.

  After collecting the normalized data and sorting it in some way, as described above, the eCDF is scatter plotted for a given test and a performance curve appears. For example, FIG. 13 shows no-step vertical jump data observed for 288 girls in the field. For a result that has not been observed, such as 26.6 inches, a rank (99.37 percentile) is assigned to the value by interpolation, and in FIG. 13, the assigned rank is not observed. Points are shown.

  For each test, an “upper limit” and “lower limit” are determined, which represent the scoring range for each test. Any test value above the upper limit will get the same number of event points. Similarly, any test value below the lower limit will get the same number of event points. These upper and lower limits maintain the integrity of the evaluation scale. The upper limit suppresses the probability that an unusual test result will distort an athlete's assessment and obscure the mediocre performance in other tests.

  Each rank is converted to an event point including a fractional part using a statistical function, which is performed in relation to the inverse Weibull transformation as described above. In FIG. 13, a scoring curve for a girl's no-step vertical jump is shown, and as shown, points are displayed as percentages. For example, 0.50 points (assigned for a 18.1 inch jump) are displayed as 50%. Event points that include these fractional parts are also referred to as w-scores (w is an acronym for Weibull).

  Inverse Weibull transform can process non-normal (distorted) distribution test data as described above. This transformation allows incremental scoring above the performance range. Gradual scoring assigns points progressively (i.e., progressively more) for more exceptional test results. This gradual nature is shown in FIG. 13 for jumps higher than 26 inches, with the red curve gradually increasing in slope and individual data points are more differentiated. Gradual scoring makes it possible to emphasize selected performance, thereby making this assessment approach more useful as a tool for identifying talent.

  FIG. 12 identifies “Andrea Wight” as a sample athlete, and she is supposed to have flew 26.5 inches for a no-step vertical jump. This value corresponds to a w score of 1.078. The w-score for all her tests can be found by referring to the chart for each test. These w scores are shown in FIG.

  Event points, including fractional parts, are summed for each evaluation test variable to obtain the athlete's total w-score. (For example, in FIG. 14, 5.520). This sum is multiplied by a magnification factor to obtain an evaluation. Regarding the women's basketball evaluation, for example, this magnification factor is 18, and therefore, the overall athletic evaluation of Andrea Wight is 99.36 (= 5.520 * 18).

  The “event magnification factor” is determined for each evaluation item according to the number of evaluated events and a desired evaluation range. The rating should generally fall in the range of 10 to 110. Since the evaluation consists of four variables, peak power, maximum touch, lane agility, and three-quarter coat sprint, the boy's magnification factor is 25, for example.

  If a female athlete “has reached the upper limit” in all six tests (FIG. 15), her total w-score is approximately 130 (129.85).

Regardless of the athlete's gender, Table 1 outlines an exemplary test sequence for each of the tests described above and assigns a time to perform each test.

  By assessing the various scores of each test, the athlete is provided with a comprehensive athletic assessment, which the athlete can compare to his ability and / or performance with that of other athletes in his age group. Can be used. In addition, athletes use this information to compare their skill sets with those of NBA and WNBA players, and how they compare their skill sets with those of professional basketball players. Can be guessed.

  Referring to FIG. 16, an exemplary method 100 for synthesizing athletic assessment scores in accordance with an embodiment of the present invention is shown. The athletic evaluation score can be synthesized in relation to a defined event such as basketball for a specific athlete. Such an athlete evaluation score can be used, for example, to ascertain the athlete's athletics and / or to compare multiple athletes. First, as shown in step 110, athletic performance data for a particular event is collected from a set of athletes. As a non-limiting example, athletic performance data includes the height of a no-step vertical jump, the height of an approach jump reach, the sprint time for a predetermined distance, the lap time for a predetermined course, and the like. Athletic performance data can be recorded for groups of athletes of hundreds or thousands of athletes. Such athletic performance data can be stored in a data store such as the database 212 of FIG.

  At step 112, the collected athletic performance data, such as athletic performance test results, is normalized. In this way, the athletic performance results (eg, raw test results) of each athletic test performed by the athlete for the defined event are normalized. That is, the raw test results of each athlete can be standardized according to a common scale. Standardization allows comparison of data corresponding to different athletic tests. In one embodiment, the standardized athletic performance data is an empirical cumulative distribution function (ECDF) percentile. One skilled in the art will recognize that any technique can be used to obtain normalized athletic performance data (ie, normalized athletic performance data).

  In step 114, the normalized athletic performance data is used to synthesize a set of ranks. The set of ranks includes the ranks included in the scoring table assigned for each athletic performance test result. A scoring table (eg, a quick reference table) includes a set of athletic performance test results or their possible results. Each athletic performance test result in the scoring table corresponds to an event point that includes an assigned rank and / or fractional part. In one embodiment, the athletic performance data is sorted and an empirical cumulative distribution function (ECDF) percentile is calculated for each value. Thus, the percentile of the empirical cumulative distribution function represents the rank for a particular athletic performance test result included in the scoring table. In this relationship, each athletic performance test result is assigned a ranking number based on the percentile of the test result in a normal distribution of test results. This rank (eg, percentile) depends on the raw test measurements and is a function of both the size of the data set and the test values that make it up. As can be seen, the scoring table can include observed athletic performance test results and unobserved athletic performance test results. Ranks corresponding to unobserved athletic performance test results can be assigned by interpolation from data on observed athletic performance tests.

  In step 116, an event point value including a fractional part is determined for each athletic performance test result. An event point value including a fractional part for a particular athletic performance test result is determined or calculated based on the corresponding assigned rank. That is, the assigned rank set is converted to an appropriate point scale. In one embodiment, a statistical function such as an inverse Weibull transform provides such a transformation.

  In step 118, one or more scoring tables are synthesized. As previously mentioned, a scoring table (eg, a quick reference table) includes athletic performance test results or a set of their possible results. Each athletic performance test result in the scoring table corresponds to an event point that includes an assigned rank and / or fractional part. In some cases, a single scoring table that includes data associated with multiple tests and / or events can be synthesized. In addition, a plurality of scoring tables can be synthesized. For example, scoring tables can be synthesized for each eye or each athletic performance test. One or more scoring tables or portions thereof (athletic test results, assigned ranks, event points including fractional parts, etc.) can be stored in a data store such as database 212 of FIG.

  As indicated in step 120, reference is made to (eg, received, acquired, retrieved, identified, etc.) athletic performance test data for a particular athlete. That is, reference is made to athletic performance test results for a plurality of different athletic performance tests. A set of athletic tests can be pre-defined for a specific event or athletic activity. An athletic performance test is designed to assess an athlete's athletic ability and / or performance, and measures athletic performance skills for a particular event or athletic activity.

  Reference athletic performance data can be measured and / or collected in the field of test events. Such data can be entered via a handheld device (eg, remote computer 216 of FIG. 17) or other computing device (eg, control server 210 of FIG. 17) and database (eg, of FIG. 17). Recorded in the database 212). In this way, data can be stored in the data store of the device that receives the input (eg, remote computer 216 in FIG. 17 or control server 210 in FIG. 17). Alternatively, the data can be stored in a data store that is remote from the device receiving the input. In such cases, the device that receives the input sends the data to the remote data store or the computing device associated therewith. By way of example only, an assessor can enter athletic performance data into the handheld device. After entering the data into the handheld device, the data can be sent to a control server (eg, control server 210 in FIG. 17) for storage in a data store (eg, database 212 in FIG. 17). The collected data can be displayed remotely on or in relation to the handheld device.

  At step 122, event point values including a fractional part corresponding to each athlete test result are identified. The scoring table can be used to retrieve or identify event point values including fractional parts based on the athlete's athletic performance test results. In an embodiment, the best result from each test is converted to an event point value that includes a fractional part by referring to the test result in a quick look-up table for each test. The method 100 generally uses a scoring table with event point values including a rank and a fractional part corresponding to each test result used to retrieve event point values including a fractional part for a particular athletic performance test result. However, other methods can be used to identify or determine the event point value including the fractional part of the test result. For example, in some cases, an event point value that includes a rank and / or fractional part may be determined after receiving the athlete's test results. From this point of view, an algorithm for calculating an event point value including a decimal part for a specific athletic performance test result can be executed in real time. By way of example only, athletic performance test results for a particular athlete can be compared to the distribution of the test results of athletic data for athletes similar to that athlete, and to determine the percentile ranking for that test result Can do. Thereafter, the percentile ranking for the test result can be converted to an event point value including a fractional part.

  In step 124, event point values, including fractional parts for each test result with relevance for the athlete, are combined or aggregated to generate a total point score. That is, the athlete's event point values including the fractional part for each test result are summed to calculate the athlete's total point score. In step 126, the total point score is multiplied by the event magnification factor to obtain an overall athletic assessment. The event magnification factor can be determined according to the number of events evaluated and / or the desired evaluation range. The athletic data associated with a particular athlete, such as athletic test results, rank, event point value including fractional part, total point value, overall athletic evaluation, etc. may be stored in a data store such as database 212 of FIG. it can.

  Having briefly described embodiments of the present invention, the following describes an exemplary operating environment suitable for implementing embodiments of the present invention.

  Referring to FIG. 17, an athletic performance information computing system environment, which is an exemplary computing system environment in which embodiments of the present invention may be implemented, is indicated generally by the reference numeral 200. Those skilled in the art will appreciate that the illustrated athletic performance information computing system environment 200 is only one example of a suitable computing environment and does not imply any limitation with respect to the scope or functionality of utilizing the present invention. You will understand. Similarly, the athletic performance information computing system environment 200 should not be construed as having any single or combination of components as a prerequisite or requirement.

  The invention may be operational with a variety of other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments and / or configurations that may be suitable for cooperation with the present invention include the following. Personal computers, handheld devices, laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and any of the above A distributed computing environment including a system or device.

  The invention can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include, but are not limited to, routines, programs, objects, components, and data types that perform particular tasks or implement particular abstract data structures. The invention may also operate in a distributed computing environment where tasks are performed by remote processing devices that are linked through a communications network. Under a distributed computing environment, program modules may be located in conjunction with local and / or remote computer storage media such as memory storage devices.

  With further reference to FIG. 17, an exemplary athletic performance information computing system environment 200 includes a general purpose computing device as a control server 210. The components of the control server 210 can include, but are not limited to, a suitable system bus for coupling various components of the system such as the processing unit, internal system memory and database cluster 212 with the control server 210. . The system bus can be any of several bus structures and can include a memory bus or memory controller, a peripheral bus, and a local bus using any of several bus architectures. As a non-limiting example, such architectures, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronic Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus (also called mezzanine bus) is included.

  The control server 210 typically contains or has access to a variety of computer readable media, such as the database cluster 212. Computer readable media can be any available media that can be accessed by server 210 and includes both volatile and nonvolatile media, removable and non-removable media. By way of non-limiting illustration, computer readable media can include computer storage media. Computer-readable media includes, but is not limited to, volatile and non-volatile media, removable and non-removable media, implemented using techniques or techniques for storage of information such as computer-readable instructions, data structures, program modules, and other data. Can be included. In this context, computer storage media can be RAM, ROM, EEPROM, flash memory or other technology memory, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage. Alternatively, other magnetic storage devices or any other medium that can be used to store desired information and that can be accessed by the control server 210 can be included without limitation. By way of non-limiting illustration, communication media includes wired media such as wired networks and direct wire connections, and wireless media such as acoustic, RF, infrared and other wireless media. Any combination of the above can also be included in the category of computer-readable media.

  The computer storage medium described above and shown in FIG. 17, including database cluster 212, provides storage for computer readable instructions, data structures, program modules and other data for control server 210. To do. The control server 210 can operate in the computer network 214 using logical connections to one or more remote computers 216. The remote computer 216 can be located in a variety of locations in athletic and training or performance environments. The remote computer 216 can be a handheld computing device, a personal computer, a server, a router, a network PC, a peer device, other common network nodes, etc., and is one of the elements described in relation to the control server 210. Part or all. The devices can be devices such as personal digital assistants.

  The exemplary computer network 214 can include, in a non-limiting sense, a local area network (LAN) and / or a wide area network (WAN). Such networking environments are commonplace in offices, corporate networks, intranets and the Internet. When operating in a WAN networking environment, the control server 210 can include a modem and other means for establishing communications over a WAN, such as the Internet. In a networked environment, program modules or portions thereof may be stored in the context of control server 210, database cluster 212, or any remote computer 216. For example, in a non-limiting sense, a variety of application programs can reside on memory associated with one or more remote computers 216. Those skilled in the art will appreciate that the network connections shown are exemplary and other means of establishing a communications link between the computers (eg, between the control server 210 and the remote computer 216) may be used. You will understand.

  In operation, athletic performance evaluators (eg, coaches, recruiters, etc.) send commands and commands to control server 210 through input devices such as keyboards, pointing devices (commonly referred to as mice), trackballs or touchpads. Information can be entered or commands and information can be communicated to the control server 210 via one or more remote computers 216. Other input devices include, in a non-limiting sense, microphones, parabolic antennas, scanners, and the like. Commands and information can also be sent directly from the athletic performance device to the control server 210. In addition to the monitor, the control server 210 and / or the remote computer 216 can include other output peripherals such as speakers and printers.

  Although many of the control server 210 and other internal components of the remote computer 216 are not shown, those skilled in the art will appreciate that such components and their interconnections are well known. Accordingly, additional details about the internal configuration of the control server 210 and the remote computer 216 will not be described in further detail here.

  In other embodiments, different tests can be performed to determine an athlete's athleticism for different events. For example, in the first pitch softball event, the method may involve conducting the test to determine the overall athleticism of the woman for the event using four separate tests. . Specifically, the athletic test measures the athlete's vertical jump height, measures the total time taken to complete the agility shuttle, measures the time taken to sprint 20 yards, and It may include measuring the flying distance of a rotating power ball throw.

  A vertical jump is a jump from an upright posture similar to the no-step vertical jump described above. A 20 yard dash is a sprint in which time is measured.

  In the agility shuttle, it is 5-10-5 agility test. Three cones (lines or other obstacles) are placed in a straight line at a distance of 5 yards from each other. Athletes begin to move from the center cone, with one hand touching. At the start, the athlete is not allowed to face or lean forward towards any of the outer cones. At the beginning of movement, the athlete sprints towards the outer cone on the opposite side of the hand that originally touched the cone. The athlete touches this outer cone, reverses direction and sprints towards the other outer cone. Once the cone is touched, the athlete turns again and runs over the central cone. The measurement time starts when the athlete releases his hand from the central cone and ends when he runs too far through the central cone.

  Rotating powerball throws can be performed using a 3 kilogram powerball. The athlete begins to move by standing at a right angle to the start line in a manner similar to a softball hitting stance. Athletes can step on or touch the start line but cannot cross the line. The ball is held with both hands, and the athlete's rear hand (the one with the palm facing the start line) has the back of the ball, and the front hand has the bottom of the ball. The ball is pulled while maintaining the ball between the athlete's waist and chest. Athlete's arm should be fully extended except for slight elbow flexion. In one action, the athlete rotates his body and swings the ball forward, preferably at an angle of 45 °. This movement reproduces the swing of a bat in a softball. The athlete ends with his arms extended. An athlete can follow through, but the athlete's feet must not extend beyond the line until the ball is released. The distance traveled by the ball is measured.

  Athletic data is captured in a manner similar to collecting basketball test data. For example, data can be entered into a handheld computing device. Two challenges are allowed for each test, and the best result is used to calculate the evaluation described below.

  By referring to the test results in a scoring table (quick reference table), the best result for each test is converted into an event point including a decimal part. In FIG. 18, an exemplary chart is shown, which provides a performance assessment for each of a series of tests for female athletes. Similar to the table for basketball (Figure 10), this quick reference table measures and records normative test data for athletes in the 100s to thousands of people, and sorts the performance according to the test range. It can be determined by identifying and establishing percentile rankings and thresholds for each of the test values observed in the test for athletes. Also, as described above, scoring is done for tabularized rankings, and a scoring chart for each scoring for each test (vertical jump, agility shuttle, 20 yard dash and rotating powerball throw) is built. Can be converted to points using statistical functions.

  FIG. 19 provides an example of collected data. The test units in FIG. 19 are as follows: VJ = vertical jump (unit: inches); Agility Shuttle (unit: seconds); 20-yard Dash (20 yards dash) ( Unit is seconds); RoPB Throw (rotary power ball throw) (unit is feet). First, the best result from each test is converted into an event point including a decimal part by referring to a scoring table (a quick reference table).

  As described above with completeness, in FIG. 20, the rank assigned to each test can be derived from normative data that is sorted and converted to a percentile of the eCDF function. As detailed above, after the normative data has been collected and sorted, the eCDF is scatter plotted to obtain a performance curve. For example, the vertical jump data observed in the field for 1343 girls is shown by a curve indicated by light blue diamonds in FIG. For unobserved results, the rank of the value is assigned by interpolation, and the yellow triangle in FIG. 21 represents an unobserved point that requires the assigned rank. The upper limit value and the lower limit value are set as described above.

  As described above, each rank is converted to an event point including a decimal part using a statistical function, for example, using inverse Weibull transformation. The event point scoring curve for a vertical jump is represented by a red circle in FIG. 21 and the points are displayed as a percentage, for example, a 19.1 inch 61st percentile jump for a female fast pitch softball. For, 0.50 points are expressed as 50%. Again, the event point including the decimal part is the w score. In a manner similar to the curve for basketball, the inverse Weibull transform can handle non-normal (distorted) distributions of test data and is evident from the slope of the w-score curve between 26 and 27 inches As such, the inverse Weibull transform allows gradual scoring to emphasize selected performance.

  Referring to FIG. 19, the sample athlete “Mariah Gearhart” is supposed to have flew 24.6 inches in a no-step vertical jump. This value corresponds to a w score of 1.023 (FIG. 20). All w-scores for her are obtained by referring to the chart for each test. These w scores are shown in FIG.

  To achieve scaling, the event points, including the fractional part, are summed for each evaluation test variable, resulting in an athlete total w score of 3.528 for the sample athlete Mariah Gearhart, as in FIG. . This sum is multiplied by an event magnification factor to obtain an evaluation. With regard to the evaluation regarding the female first pitch, for example, this magnification factor is 30, so the overall athletic evaluation of Mariah Gearhart is 105.84 (= 3.528 × 30).

  The “event magnification” is determined for each evaluation based on the number of events evaluated and the desired evaluation range. Evaluation is generally between 10 and 110. If a female athlete “achieves the upper limit” in all four tests (as in FIG. 23), her total w-score is rated as 157.44 (= 5.248 × 30). Let's go. In one embodiment, an upper limit (eg, 120) can be imposed to limit the total score for extreme outliers.

In another embodiment, different tests can be performed to determine an athlete's athletics for football. Specifically, the athletic performance test involves measuring the athlete's vertical jump height, measuring the time taken to complete the agility shuttle, kneeling power vaults, and the time taken to sprint 40 yards. And peak power vertical jumps can be included. The agility shuttle is described above and the 40 yard dash is similar to the 20 yard dash described above. The kneeling power vault is performed by throwing a 3 kilogram power ball from the chest at the kneeling position. This movement is similar to a two-handed chest pass in basketball except that it is kneeling and the ball trajectory used to obtain maximum flight distance is 30-40 degrees above the horizontal. Power vertical jump measures the peak power of the lower body and adds weight to the vertical distance. In an embodiment, a contact mat is used to determine the vertical distance of the jump. The power vertical test can incorporate weight as an initial event result in some approaches. In other embodiments, only vertical jumps can be used. As an example of a power vertical test incorporating weight, the following formula can be used as the peak power event result:

In the above formula, Peak Power (watts) is peak power (unit is watts), Vertical Jump (cm) is vertical jump (unit is centimeter), and Weight (kg) is body weight (unit is kilogram). ).

  In the football embodiment, the results are processed using the systems and methods described above.

  Although the present invention has been described in the context of particular embodiments, these are not intended to be limiting in all respects, but are intended to be exemplary. Alternate embodiments will become apparent to those skilled in the art without departing from the scope of the invention.

  In view of the above, and with the advantages that are apparently or inherently obtained from the systems and methods of the present invention, it is understood that the present invention is suitably adapted to accomplish all of the above-mentioned objectives. It will be. It will be understood that particular features and particular sub-combinations have utility and can be utilized without reference to other features and sub-combinations. This is considered in the claims.

Claims (16)

One or more computer storage media comprising computer-executable instructions for performing a method for assessing an athlete's athleticism in an event defined in a computing environment;
The method
Receiving at least two results relating to the performance of the athlete in at least two different athletic performance tests associated with the defined event;
Comparing each of the at least two results to a distribution of test results of athletic data of athletes similar to the athlete to determine a percentile ranking for each of the at least two results;
Converting the percentile ranking for the at least two results into an event point value including a fractional part for each result; and combining the event point value including the decimal part and using a scaling factor to define the defined item Generating an athletic assessment score for the athlete at
One or more computer storage media.   The one or more computer storage media of claim 1, wherein the percentile ranking for the at least two results is gradual.   3. The one or more computer storage media of claim 2, wherein converting the percentile ranking for the at least two results to an event point value including the fractional part involves applying a reverse Weibull transform.   The one or more computer storage media of claim 1, wherein a distribution of athletic data test results for athletes similar to the athlete is determined using an empirical cumulative distribution function.   The one or more computer storage media of claim 1, wherein the percentile ranking for the at least two results is limited with an upper limit and / or a lower limit.   The one or more of claim 1, wherein the defined event is basketball and the at least two athletic performance tests include a no-step vertical jump test, an approach jump reach height test, a sprint time test, and a lap time test. Computer storage medium.   The athletic data test results of athletes similar to the athlete include data from athletes of the same gender as the athlete and / or data from athletes belonging to an age range that includes the age of the athlete. One or more computer storage media. An athletic ability evaluation system for evaluating an athlete's athleticism in a defined event, the athletic ability evaluation system comprising:
Obtaining the athlete's performance in at least two different athletic performance tests related to the defined event as measured by a test facility or equipment and defining results for each performance test;
Comparing the results for each performance test with a distribution of test results for athletic data of athletes similar to the athlete to determine a percentile ranking for each result for the performance test;
The percentile ranking for each result is converted to an event point value including a fractional part for each result, and the event point value including the decimal part is combined, and the scaling factor is used to An athletic ability evaluation system, comprising: generating an athletic evaluation score for an athlete. 9. The athletic performance evaluation system of claim 8, wherein the percentile ranking for each result for the performance test is gradual. The athletic ability evaluation system according to claim 9, wherein the percentile ranking for each result for the performance test is limited by a lower limit and an upper limit. Measuring the athlete's performance with the test facility or equipment ,
Measuring the height of the athlete's no-step vertical jump;
Measuring the height of the athlete's approach jump reach;
Measuring a sprint time for a predetermined distance of the athlete; and measuring a lap time for a predetermined course of the athlete;
The athletic ability evaluation system according to claim 9. Measuring the athlete's performance with the test facility or equipment ,
Measuring the weight of the athlete; and calculating peak power based on the weight and height of the no-step vertical jump;
The athletic ability evaluation system according to claim 11. The athletic data test results of athletes similar to the athlete include data from an athlete of the same gender as the athlete and / or data from an athlete belonging to an age range that includes the age of the athlete. Athletic ability evaluation system . The defined event is softball,
Measuring the athlete's performance with the test facility or equipment ,
Measuring the height of the athlete's no-step vertical jump;
Measuring the lap time for a predetermined course of the athlete;
Measuring the sprint time for a predetermined course of the athlete; and measuring the athlete's rotational power ball throw distance;
The athletic ability evaluation system according to claim 9. The defined event is football,
Measuring the athlete's performance with the test facility or equipment ,
Measuring the vertical jump height of the athlete;
Measuring the lap time for a predetermined course of the athlete;
Measuring the sprint time for a predetermined course of the athlete; and measuring the athlete's kneeling powerball throw distance;
The athletic ability evaluation system according to claim 9. An athletic ability evaluation system for evaluating an athlete's athleticism in a defined event, the athletic ability evaluation system comprising:
Referencing a first athletic performance test result and a second athletic performance test result for the defined event corresponding to the athlete;
Identifying an event point value including a decimal part using the first athletic performance test result and identifying an event point value including a second decimal part using the second athletic performance test result; Wherein the event point values including the first and second fractional parts are identified using a scoring table including a plurality of test results and event point values including the corresponding decimal parts;
Summing the event point value including the first decimal part and the event point value including the second decimal part to obtain a total point value for the athlete in the defined event, and using an event magnification factor wherein performing scaling the total point value, a system for implementing the steps of synthesizing an overall Asurechisumu evaluation scores for the athletes in events that defined Te,
The event point value including the fractional part is derived based on the ranking assigned for the corresponding test result.
This is an athletic ability evaluation system .