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WO2003088584A1 - Systeme utilisable pour gestionner et fournir des resultats, des pronostics et des informations de jeux et paris pour des evenements sportifs geographiquement disperses - Google Patents

Systeme utilisable pour gestionner et fournir des resultats, des pronostics et des informations de jeux et paris pour des evenements sportifs geographiquement disperses Download PDF

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Publication number
WO2003088584A1
WO2003088584A1 PCT/SE2003/000516 SE0300516W WO03088584A1 WO 2003088584 A1 WO2003088584 A1 WO 2003088584A1 SE 0300516 W SE0300516 W SE 0300516W WO 03088584 A1 WO03088584 A1 WO 03088584A1
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Prior art keywords
prognosis
time
athlete
games
geographically distributed
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English (en)
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Lennart Isaksson
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Classifications

    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0084—Exercising apparatus with means for competitions, e.g. virtual races
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
    • A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
    • A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
    • A63B71/0622—Visual, audio or audio-visual systems for entertaining, instructing or motivating the user
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2102/00—Application of clubs, bats, rackets or the like to the sporting activity ; particular sports involving the use of balls and clubs, bats, rackets, or the like
    • A63B2102/32—Golf
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/62—Time or time measurement used for time reference, time stamp, master time or clock signal
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2225/00—Miscellaneous features of sport apparatus, devices or equipment
    • A63B2225/20—Miscellaneous features of sport apparatus, devices or equipment with means for remote communication, e.g. internet or the like
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2225/00—Miscellaneous features of sport apparatus, devices or equipment
    • A63B2225/50—Wireless data transmission, e.g. by radio transmitters or telemetry
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2230/00—Measuring physiological parameters of the user
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2244/00—Sports without balls
    • A63B2244/19—Skiing
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B69/00—Training appliances or apparatus for special sports
    • A63B69/0028—Training appliances or apparatus for special sports for running, jogging or speed-walking

Definitions

  • the present invention relates in a first aspect to a system operable to manage and provide results-, prognosis and games & gaming information.
  • the present invention relates to a method for managing and providing results-, prognosis and games & gaming information
  • the present invention relates to at least one computer program product for managing and providing results-, prognosis and games & gaming information.
  • the spectators off-site are for major national events provided with result service through radio- or television, which in some cases could be an alternative also for the on-site spectators.
  • This information is however distributed in broadcast mode which many times don't match the information requirement from spectators at different locations.
  • information from athletic events have been made available on the web. Large problems with existing solutions are that they don't support:
  • GSM Global System for Mobile communications
  • Leader Board present for each golf player the golf score on each individual golf hole. For each golf hole, the par-result is also presented. If a player has achieved results better or worse than par, specific symbols (rings- and squares) is placed around the players result) where the result presentation is continuously sorted based on prognosis for the outcome of the competition.
  • System architecture that makes it possible to update leader-board continuously in real time instead of sync-mode.
  • a system operable to manage and provide results-, prognosis and games & gaming information in real time for geographically distributed sport events.
  • the system comprises a number of wireless communication devices, each of which is identified with a unique identification code and at least one located at each geographically distributed event, for transmitting results/information to at least one control means which is connected to a first memory system for storing said results/information.
  • the system also comprises a to said at least one control means and to said first memory means connected computing means operable to, based on said results/information, calculate a prognosis of an outcome of said events with the aid of an iterative and adaptive algorithm.
  • the system also comprises a to said computing means connected second memory system for storing said prognosis, wherein said second memory system is connected to said at least one control means, which in turn transmits said results/information and/or prognosis to users of said system via a mobile or fixed network.
  • This system makes it possible to geographically dispersed spectators to receive full access information regarding progress/sub-results during competitions in geographically distributed events.
  • the system also makes it possible for geographically dispersed spectators to receive prognosis regarding geographically distributed events and attend interactive games & gaming applications.
  • a further advantage in this context is achieved if each memory system comprises a number of geographically distributed databases.
  • said system also comprises different types of geographically distributed sensors operable to collect different types of information from said geographically distributed events.
  • a further advantage in this context is achieved if one type of sensor is a time measuring sensor, and in that an internal clock of an individual sensor is calibrated through a circuit switched synchronization pulse.
  • one type of sensor can be a biometric sensor.
  • a further advantage in this context is achieved if said identification code is based on A-number and/or IP-address and/or individual device identification code.
  • a further advantage in this context is achieved if said A-number and/or IP- address and/or individual device identification code is combined with user and password identification or electronic certificate.
  • said distributed events are sport events where the performance is measured by used time
  • said iterative and adaptive algorithm for calculating a prognosis can be expressed as:
  • N is the number of time controls in the competition; L is the number of the last time control the athlete in question has passed; k (L, S) is an adjustable matrix of variables for each time control prognosis calculation; P (Athlete, M) is the prognosis for the athlete in question at time control M, wherein M is an integer and L + 1 ⁇ M ⁇ N; Time(Athlete, L) is measured time between the last time control (L) the athlete in question has passed and the preceding one, and ATime (M) is the aver- age time between time control M and the preceding time control for athletes who has passed time control M.
  • L is the number of the time control last passed by the athlete for which the prognosis is calculated
  • X is the number of time controls for which the tendency is calculated between the athlete in question and all other athletes that has passed the time control for which the prognosis is calculated (L + 1).
  • said recursive algorithm for calculating a prognosis is complemented with individual weighting factors for each athlete, wherein said weighting factors are based on the athletes individual performance characteristics during similar races.
  • a further advantage in this context is achieved if said computing means calibrates said k (L, X) matrix and said weighting factors after a competition/race is finished. Furthermore, it is an advantage in this context if said distributed events is a golf tournament, and in that said recursive algorithm for calculating a prognosis can be expressed as:
  • N is the number of holes in a competition
  • L is the last hole reported for the player in question
  • k (L, X) is an adjustable matrix of variables for each golf hole prognosis calculation
  • AScore Player, L is last reported golf score for the-player in question for hole L.
  • Another object of the invention is to provide a method for management and to provide results-, prognosis and games & gaming information in real time from geographically distributed events with the aid of a system comprising a number of wireless communication devices, at least one of which is located at each geographically distributed event.
  • the method comprises the steps of: - with the aid of said wireless communication devices, to transmit results/information to at least one control means;
  • each memory system comprises a number of geographically distributed databases, wherein said storing steps consists of:
  • said system also comprises different types of geographically distributed sensors, wherein the method also comprises the steps:
  • said sensors collects different types of information from said geographically distributed events; and - to send said collected information to said at least one control means.
  • a further advantage in this context is achieved if one type of sensor is a time measuring sensor, and in that said method also comprises the step:
  • each wireless communication device is identified with a unique identification code based on A-number and/or IP-address and/or individual device identification code. Furthermore, it is an advantage in this context if said A-number and/or IP- address and/or individual device identification code is combined with user id and password identification and/or electronic certificate.
  • a further advantage in this context is achieved if said distributed events are sport events where the performance is measured by used time, and in that said calculation step is performed by:
  • P (Athlete,L+1) ⁇ Time (Athlete, X) + ATime (L+1) * ⁇ k (L,X)* Time (Athlete, X)/ATime(X) ⁇
  • N is the number of time controls in the competition; L is the number of the last time control the athlete in question has passed; k (L, S) is an adjustable matrix of variables for each time control prognosis calculation; P (Athlete, M) is the prog- nosis for the athlete in question at time control M, wherein M is an integer and L + 1 ⁇ M ⁇ N; Time(Athlete, L) is measured time between the last time control (L) the athlete in question has passed and the preceding one, and ATime (M) is the average time between time control M and the preceding time control for athletes who has passed time control M.
  • said method also comprises the step:
  • L is the number of the time control last passed by the athlete for which the prognosis is calculated
  • X is the number of time controls for which the tendency is calculated between the athlete in question and all other athletes that has passed the time control for which the prognosis is calculated (L + 1).
  • a further advantage in this context is achieved ifsaid recursive algorithm for calculating a prognosis is complemented with individual weighting factors for each athlete, wherein said weighting factors are based on the athletes individual performance characteristics during similar races. Furthermore, it is an advantage in this context if said method also comprises the step:
  • N is the number of holes in a competition
  • L is the last hole reported for the player in question
  • k (L, X) is an adjustable matrix of variables for each golf hole prognosis calculation
  • AScore (Player, L) is last reported golf score for the player in question for hole L.
  • said method also comprises the step: - with the aid of said computing means, to calibrate said k (L, X) matrix after the competition is finished.
  • Another object of the invention is to provide at least one computer program product directly loadable into the internal memory of at least one digital com- puter.
  • the at least one computer program product comprises software code portion for performing the steps of the method according to the present invention, when said at least one product is/are run on said at least one computer.
  • the computer program product(s) makes it possible for geographically dispersed spectators to receive full access information regarding progress/sub-results during com- petitions in geographically distributed events.
  • the product(s) also makes it possible for geographically dispersed spectators to receive prognosis regarding geographically distributed events and attend interactive games & gaming applications. It should be emphasised that the term "comprises/comprising" when used in this specification is taken to specify the presence of stated features, steps or components but does not preclude the presence of one or more other features, integers, steps components of groups thereof.
  • Figure 1 shows a block diagram of a system that manages and provides results-, prognosis and games & gaming information in real time according to the present invention
  • Figure 2 is a flow chart of a method for providing results/information and prognosis according to the present invention
  • Figure 3 shows a schematic diagram of some computer program products according to the present invention
  • Figure 4 shows a schematic diagram of distributed servers, databases and modem pools for different regions and network connection types according to the present invention
  • Figure 5 Shows the optimised database storage structure according to the present invention.
  • Figure 6 shows a table illustrating the prognosis calculation according to the present invention
  • Figure 7 shows a schematic diagram of a track with a number of time controls
  • Figure 8 is a flow chart of calibration of time measurement data from distributed sensors calculation according to the present invention
  • Figure 9 is a flow chart of correction of collected time results before presentation according to the present invention
  • Figure 10 is a flow chart of approval procedure for markers according to the present invention
  • Figure 11 is a flow chart of transmission of golf score results from approved markers according to the present invention
  • Figure 12 is a flow chart of Leader Board presentation to approved users according to the present invention.
  • Figure 13 is a flow chart of payment set-up for users that would like to play games- and gaming applications based on the result information according to the present invention
  • Figure 14 is a flow chart of collection of golf results for determination which players has made correct tips and gets invited to continue the game according to the present invention
  • Figure 15 is a flow chart that request new tip from gamblers qualified to continue the interactive games- and gaming application according to the present invention.
  • Figure 16 is a flow chart that creates tip for qualified gamblers who have not submitted new tip on time according to the present invention.
  • Figure 1 shows a block diagram of a system operable to provide results/information and prognosis from geographically distributed events according to the present invention.
  • the system 10 comprises a number of wireless commu- nication devices 12* ⁇ , ..., 12 n , each of which is identified with a unique identification code and at least one located at each geographically distributed event.
  • n different wireless communication devices 12 ⁇ 12 n each of which transmits results/information to at least one control means 14 which is connected to a first memory system 16 for storing said results/information.
  • control means 14 for the sake of simplicity, there is only disclosed one control means 14 in figure 1.
  • the system 10 also comprises a to said control means 14 and to said first memory system 16 connected computing means 18 operable to calculate a prognosis of an outcome of said events with the aid of a recursive algorithm. The calculation is based on said results/information.
  • the system 10 also comprises a to said com- puting means 18 connected second memory system 20 for storing said prognosis.
  • the second memory system 20 is also connected to the control means 14.
  • the control means 14 transmits said results/information and/or prognosis to users of the system 10 via a mobile or fixed network.
  • the users are diagrammatically dis- closed in figure 1 with a number (m) of wireless communication devices 22 ⁇ , ..., 22 m .
  • each memory system 16, 20 comprise a number of geographically distributed databases. (See figure 4 and 5.)
  • system 10 also comprises different types of geographically distributed sensors (not disclosed) operable to collect different types of information from said geographically distributed events.
  • Said sensors can be connected to or integrated with said wireless communication devices 12 ⁇ , .... 12 n .
  • system 10 is one type of sensor a time measuring sensor, and an internal clock of an individual sensor is calibrated through a circuit switched synchronization pulse.
  • one type of sensor be a biometric sensor.
  • system 10 is the identification code based on A number and/or IP address and/or individual device identification code. In yet another embodiment of the system 10 is the identification code based on A number and/or IP address and/or individual device identification code and/or electronic certificate.
  • the A number and/or IP address and/or individual device identification code and/or electronic certificate combined with user and password identification.
  • figure 6 there is disclosed a table illustrating the prognosis calculation according to the present invention.
  • Prognosis calculation for one time control ahead For Athlete B the prognosis algorithm calculates how each of the part times for athlete B correlates with the athletes who have passed the goal (in this case only athlete A).
  • the prognosis calculation compares the progress between every pair of time controls for the actual athlete (B in this example) and the athlete/athletes that have passed the time control for which the prognosis is calculated (time control 6 - the Goal which only athlete A has passed in this example). Each of the comparison results is multiplied with a weight factor k(,). The comparison results represents the actual tendency between actual athlete (B in this example) and the ath- lete/athletes passed next time control (A in this example) for every pair of time controls passed so far.
  • the correlation is calculated for each pair of part time controls both A and B in this example has passed.
  • the algorithm makes it possible to give higher priority to the correlation between the ath- letes at the later part of the race.
  • the algorithm estimate the time for athlete B from time control 5 to the goal by a calculation assuming the tendency between the athletes progress will remain during the last part of the race.
  • Prognosis calculation for two- or more time controls ahead For Athlete C, the correlation calculation is made with all athletes passing time control 5 (in this case Athlete A and B). When the prognosis for time control 5 has been calculated, that time is used as "real time” for Athlete C at time control 5 when the prognosis algorithm is applied again for athlete C calculating the prognosis for time control 6 - the goal. For the prognosis of the finish time, the correlation is only calculated for athletes who have passed the goal (only athlete A in this example).
  • the prognosis algorithm is re- structured calculating the optimal value of the "constant" weight factors that would have generated a correct prognosis for athlete C at time control 5.
  • the real time at time control 5 as well as the optimum value of the constant weight factor is then used when an up-dated prognosis for the finish time at the goal is calculated for C.
  • the same procedure is repeated for all athletes that have passed at least one time control.
  • the precision of the algorithm is highest predicting the next time control for athletes when as many athletes as passible has passed the next time control. But becau.se of the adaptive learning of the algorithm through earlier similar competitions, the quality gets high also for predictions several time controls ahead.
  • the time control for which a prognosis shall be calculated This could be any time control between the last time control passed by actual athlete to the time control at the goal of the competition.
  • Speed Average speed (meter/second) of the athlete from start to the last time control the actual athlete has passed Start_Time(Athlete) Absolute time (hour, minutes and seconds) when actual athlete started the race.
  • Delta_Time(Athlete, Next Time Control) Prognosis time (seconds) for actual athlete to move from last time control passed to the next time control.
  • Prognosis time absolute time, hour , minutes and seconds
  • the algorithms can also be used in countries not using the metric system. In those cases, meter is exchanged with yard.
  • N Used time is collected at N positions at the track where the competition takes place. The last of these is the goal/end-point for the competition. In order to make a prognosis, N has to be larger than 1. As soon as an athlete has passed a time control, the result is transferred to the prognosis algorithm for calculation of actual prognosis for the next- and all other remaining time controls.
  • k(L, X) Adjustable matrix of weight factors for each time control prognosis calculation.
  • the first variable in the matrix represents the time control number of the last time control actual athlete has passed.
  • the 2 nd vari- able in the matrix represents the value for each time control starting with the 1 st one and ending with the last one passed by actual athlete.
  • the reason for making k(,) two dimensional is the following: Calculating a prognosis after actual athlete has passed the first control with sub-result makes it possible to compare actual athlete's results with the athlete's who has passed next sub- result control only at the first sub control. When actual athlete has passed two controls, it is possible to make the comparison
  • L rows
  • M columns for a competition with 6 time controls.
  • P(Athlete, M) Prognosis for actual athlete at time control M, M is a value in the interval between L+1 to N.
  • ATime(1) average time from start to 1 st time control for athletes who has passed time control M (M is the time control for which the prognosis is calculated)
  • ATime(M) average time between time control M and the preceding time control for athletes who has passed time control M.
  • the algorithm is iterative and adaptive. That mean that if a prognosis shall be calculated for an athlete two-time control's ahead, step one is to calculate the prognosis for the next time control.
  • the prognosis value for the next time control is used as the actual time for the actual athlete reaching that time control.
  • the ATime is always calculated for the Athletes that has passed the time control for which the prognosis is calculated. The same procedure is repeated until the prognosis is calculated for the last time control. As always, when the actual Athlete has reached the next time control, all prognosis calculation for the remaining time controls are re-calculated based on the real time results so far instead of prognosis values.
  • a recommendation is that the prognosis algorithm considers only a sub-set of athletes with similar performance level at last time control passed by actual athlete, for example the five athletes with most similar time.
  • the prognosis algorithm is applied iterative for next time control, a new sub-set is selected with for example the five athletes with most similar time at that time control compared with the prognoses time for actual athlete. The same procedure is then repeated until the prognosis for the final time control is calculated for actual athlete.
  • L is the number of the time control last passed by the athlete for which the prognosis is calculated.
  • X is the number of the time controls for which the tendency is calculated between actual athlete and all other athletes that has passed the time control for which the prognosis is calculated (L+1). During the prognosis calculation, X is stepped from 1 to L.
  • Enhancement of the algorithm based on individual athletes individual performance characteristics during a race (optional) Some athletes have higher ability than others to "speed-up” during the finish part of the races. Other athletes are “always” sterting-up races at a higher relative speed than the others, and some are holding a very constant speed through the whole race.
  • Start_Up(Athlete) Quotient between the actual athlete average time from start to a time control at about 1/3 rd of the race with the same athlete total time from start to goal at the same races.
  • Prev_Part_Time(c, t, Athlete) Matrix storing results from previous competitions.
  • First variable represent the index for each of the stored competitions.
  • Second variable represent the time control index for each of the stored competitions.
  • the last variable represents the index for respective athlete in the matrix.
  • the stored value of Prev_Part_Time is the used time for actual athlete for indexed segment in indexed competition.
  • Prev_Total_Time(c, Athlete) Matrix storing results from previous competitions.
  • First variable (represented with c) represent the index for each of the stored competitions.
  • the last variable (represented by Athlete) identify the total time actual athlete has used during the specific competition
  • StartJJp(Athlete) (l/c_max)* ⁇ ⁇ Prev_Part_Time(c,t,Athlete)/Prev_Total_Time(c,Athlete) ⁇ ,
  • c_max is the total number of relevant competitions for which the calculation is done. If only selected competitions shall be considered, only the index-numbers for these specific competitions is used as index for variable c in the sum.
  • t 3 for all stored competitions.
  • AStart_Up(Athlete) Quotient between the athletes who has passed the time control for which the prognosis is calculated for average time from start to a time control at about 1/3 rd of the earlier races considered for actual athlete above with the same athletes total time from start to goal at the same earlier races.
  • Start_Up(Athletes) (1/(c_max * Nr_of_Athletes)) * cjnax, Athlete_W ⁇ ⁇ Prev_Part_Time(c,t,Athlete) / Prev_Total_Time(c, Athlete) ⁇
  • Athlete V to Athlete W The set of athletes which has passed the time control for which the prognosis is made. This is the only athletes considered during this calculation.
  • Start_Up_Factor(Athlete) Start_Up(Athlete) / AStart_Up(Athletes)
  • Mid_Factor(Athlete) Mid(Athlete) / AMid(Athletes)
  • Finish_Factor(Athlete) Finish(Athlete) / AFinish(Athletes)
  • Adjusted_P(Athlete, M) [ A1 + B1 * ⁇ Start_Up_Factor(Athlete) ⁇ ] * P(Athlete, M),
  • Adjusted_P(Athlete, M) [ A2 + B2 * ⁇ Mid_Factor(Athlete) ⁇ ] * P(Athlete, M),
  • Adjusted_P(Athlete, M) [ A3 + B3 * ⁇ Finish_Factor(Athlete) ⁇ ] * P(Athlete, M),
  • the values at each column on same row of the k(L, X) matrix is increased linear with a specific value (the numerator is increased with the value 1) for every cell when X (index the columns) is stepped from 1 to L (see figure be- low).
  • the reason for this is to give the relative performance change during later phases during the competition higher priority than the relative performance change earlier in the competition.
  • Every row in the matrix represents the weight factors utilized for prognosis calculation at a specific sub-result control at the competition. Row 1 for prognosis calculations for sub-result control 2, row 2 for prognosis calculation for sub-result control 3 etc.
  • the Value_Set that has created the lowest accumulated absolute error value is selected as the optimum value set in the k(L, X) matrix for the next competition.
  • the absolute difference between the prognosis time when the value-set is applied in the prognosis algorithm and the actual time for every athlete passing time control 4 is accumulated and stored in the vector ERROR(Value_Set), where Value_Set represent the actual value set used for the prognosis calculations (value combination in column 1 , 2 and 3 on row 3).
  • Value_Set represent the actual value set used for the prognosis calculations (value combination in column 1 , 2 and 3 on row 3).
  • the Value_Set that has created the lowest accumulated absolute error value is selected as the optimum value set in the k(L, X) matrix for next competition.
  • the prognosis for the time controls are calculated for all relevant athletes (everybody but the ones that passed the next latest time control before anybody has reached the latest time control) using value sets for the A- and B sets ranging between (1.0, 0.0), (0.9, 0.1), (0.8, 0.2), (0.7, 0.3), .... (0.1, 0.9) and (0.0, 1.0).
  • a prognos_error is calculated:
  • Prognos_Error(Athlete, N, A, B) Time(N) - P(Athlete, N),
  • N identify actual time control for which the prognosis is made
  • A represent the value of actual A1 , A2 or A3 variable B represent the value of actual B1 , B2 or B3 variable
  • F Represent the actual time control number, N is th' ⁇ total number of time controls at the specific competition.
  • A Represents the actual A-variable used for considering individual performance curb at the race.
  • B Represents the actual B-variable used for considering individual performance curb at the race.
  • the proposed value of B is set to the average optimum value for each value set generated from the preceding competitions.
  • the distributed events can be a gplf tournament.
  • the algorithm gets more complicated because larger competitions are played over four rounds, each with 18 golf holes.
  • the prognosis algorithm manages the handicap-factor by reducing the applicable handicap for each player and golf hole based on the slope for the actual golf course when performed results for each golf-hole are collected. For the remaining holes, the applicable handicap reduction for each hole is reduced for them as well making all computations based on net-results. If anyone would like to get the gross results presented, it is easily managed by adding the same handicap factor on each hole before the presentation is done.
  • the applicable handicap is calculated on each hole by considering the player's actual handicap as well as the actual golf course slope factor, which is a table that calculates the real handicap for each player based on their individual handicap corrected by the level of the actual golf course.
  • J can have values between 1 (try out faked result for next hole) and N-L (try out faked results for all remaining golf holes in the competition).
  • FScore(Player,M) Faked score for actual player on golf hole number M.
  • FP(Player, M) Prognosis on hole M based on faked sub-results for actual player.
  • M is a value between L+1 and N k(L, X) Adjustable matrix of weight factors for each golf hole prognosis calculation.
  • L represents the last golf hole played/reported for the actual player for which a prognosis calculation is done.
  • X identifies the variable value for each golf hole starting with the 1 st one and ending with the last one passed.
  • M Prognosis for actual player for not yet played/reported golf hole M, M is a value equal or between L+1 to N Score(Player, 1) Golf score at 1 st hole reported for actual player Score(Player, 2) Golf score at 2 nd hole reported for actual player
  • the algorithm is iterative and adaptive. That mean that if a prognosis shall be calculated for a player two golf holes ahead, step one is to calculate the prognosis for the next golf hole.
  • the prognosis value for the next golf hole is used as the actual result performed for actual player on that hole in the algorithm.
  • the AScore is always calculated for the Athletes that have played/reported result for the golf hole for which the prognosis is calculated. The same procedure is repeated until the prognosis is calculated for tine last golf hole.
  • all prognosis calculation for the remaining golf holes are recalculated based on the real scores achieved so far instead of prognosis values.
  • a recommendation is that the prognosis algorithm considers only a sub-set of golf players with similar performance level compared with actual athlete at the golf holes played by actual athlete so far. For example the five golf players with most similar accumulated golf score compared with actual athlete.
  • a new sub-set of golf players is selected with for example the five golf players with most similar accumulated golf scores compared with actual athlete for all golf holes played by actual athlete plus the next golf hole.
  • the accumulated golf score for actual athlete is calculated as the real golf scores for the golf holes played plus the golf score prognosis for the hole not played yet. The same procedure is then repeated until the prognosis for the last golf hole has been calculated for actual athlete.
  • the athletes as well as the spectator's on- and off site can use the prognosis service. Both for the athletes and for the spectators, it is of interest to check-out how the likely end-result will change if the actual player creates a specific result on the golf hole played right now, the next hole etc. This knowledge can influence the player's strategy significantly. The calculation of this types of requests is described below.
  • the user has proposed faked results for all remaining golf holes in the competition. It is therefore not necessary to calculate any prognosis, only to present Leader Board for the competition with the accumulated result for the holes actual player has played and the faked results for the remaining golf holes together with the results (for players who has completed their golf round) and prognosis (for players that hasn't finished their golf round yet) for the other players.
  • the values at each column on same row of the k(L, X) matrix is increased linear with a specific value (the numerator is increased with the value 1 ) for every cell when X (index the columns) is stepped from 1 to L (see figure below).
  • the reason for this is to give the relative performance change during later phases during the competition higher priority than the relative performance change earlier in the competition.
  • the complete matrix has 17 rows and 17 columns. After the competition is finished, it is recommended to try-out other values for the k(L,X) matrix using other gradient of the curb as well as exponential and 1 /exponential curbs.
  • the calibration algorithm is:
  • Every row in the matrix represents the weight factors utilized for prognosis calculation for a specific golf hole.
  • Row 1 for prognosis calculations for hole nr 2 row 2 for prognosis calculation for hole nr 3 etc.
  • the absolute difference between the prognosis score when the value-set is applied in the prognosis algorithm and the actual score for every golf player at golf hole nr 3 is accumulated and stored in the vector V i-ftJ o- used for the prognosis calculations (value combination in column 1 and 2 on row
  • the Value_Set that has created the lowest accumulated absolute error value is selected as the optimum value set in the k(L, X) matrix for the next com- petition.
  • the Value_Set that has created the lowest accumulated absolute error value is selected as the optimum value set in the k(L, X) matrix for next competition.
  • the prognosis algorithm is enhanced utilizing the fact that each golf hole is individual and match different players physical- and mental abilities differently. Some players achieve well on some holes and others play well on others. The following algorithm describe how this information is utilized:
  • the Performance matrix is calculated based on earlier relevant results on actual golf course using the following algorithm:
  • Par(X) The "par" value for the actual golf hole
  • the definition of previous golf rounds on the golf course includes as many rounds completed during applicable time period.
  • the definition of applicable time period require that the golf course has not been modified during the time period and that the players performance level has been "similar" during the time period.
  • the handicap should for example not been changed more that for example 25% during the time period.
  • the user has proposed faked results for all remaining golf holes in the competition. It is therefore not necessary to calculate any prognosis, only to present Leader Board for the competition with the accumulated result for the holes actual player has played and the faked results for the remaining golf holes together with the results (for players who has completed their golf round) and prognosis (for players that hasn ' t finished their golf round yet) for the other players.
  • RO_SUM Round Off Sum this is the accumulated value of the round off made on all previous prognoses presented holes.
  • RO_SUM P(Player, L+1) - PP(Player, L+1)
  • RO_SUM ⁇ RO_SUM + P(Player, L+2) ⁇ - PP(Player, L+2)
  • the time measuring sensors are either mobile phones/computers with internal clock, other kind of time measuring devices either connected to a mobile phone or computer with communication facilities or with own mobile communica- tion module with ability to communicate by using packet-switched oriented networks.
  • the event organizer Before the collection of measuring data, the event organizer specify the A- number and/or the IP-address for all communication devices that will distribute time result data to the computer that collect- and process the results before they are presented to the audience.
  • a sync-pulse is sent to each time measuring sensor through a circuit switched network, which can be either a fixed- or cellular network (se below figure).
  • the absolute time is stored for each sensor when the sync pulse is transmitted. If the transmission of the sync pulse is based on broadcast transmission, the distributed sync pulse is of course sent at the same time to all sensors.
  • the time measuring clock is read and the value transmitted back either trough a circuit- or packet switched network to the computer that distributed the sync pulse.
  • a calibration value Delta(Sensor) is calculated as the difference between the time value returned from the sensor and the system clock time when the synchronization signal was distributed to the sensor.
  • FIG 8 there is disclosed a flow chart of synchronization of time measurement data from distributed sensors.
  • the A-number and/or IP address is for the sensor is verified before the received data is accepted and the correction Delta are calculated.
  • userJD and password or even digital certifi- cated is used in the same way as described earlier.
  • the procedure is repeated for all sensors used in the competition. If the Send_Time_Request signal is sent through a broadcast channel, it's enough to make one transmission of the signal to calibrate the received clock values from all sensors. Every sensor distributes back the measured time when the sync pulse was received.
  • a control that the sensor has valid A- number and IP address is made before the measured data is accepted and the correction calculated.
  • a parity check is made based on the actual protocol used during the transmission in order to verify that the time value has not been disturbed during the transmission.
  • FIG 2 there is disclosed a flow chart of a method fro providing re- suits/information and prognosis in real time from geographically distributed events.
  • the method is performed with the aid of a system comprising a number of wireless communication devices 12 ⁇ , ..., 12 n (see figure 1), at least one of which is located at each geographically distributed event.
  • the method begins at block 30.
  • the method continues with the step: with the aid of said wireless communication devices, to transmit results/information to at least one control means.
  • the method continues at block 34 with the step: to store said results/information in a first memory system connected to said at least one control means.
  • the next step, at block 36, is to, based on said results/information, calculate a prognosis of an outcome of said events with the aid of a recursive algorithm.
  • the method continues at block 38 with the step: to store said prognosis in a second memory system connected to said at least one control means.
  • the next step, at block 40, is to transmit said results/information and/or prognosis to users of the system via a mobile or fixed network.
  • the method is completed at block 42.
  • figure 4 there is disclosed the principle with distributed servers, data- bases and modem pools for different regions and network connection types.
  • figure 5 there is disclosed the principle for data storage in the distributed databases in order to be able to manage data transmission/collection of sport results and games & gaming information to/from very large number of users during the very short time periods required especially by the interactive games & gaming applications.
  • all necessary data for gamblers is stored in each partition of the database where the partitions are based on actual tip during last interactive session.
  • This database structure makes it possible to distribute tip- requests directly to the gamblers that made the correct tip during last game session (called approved gamblers) without wasting time running through selection analysis in order to identify which gamblers actually are approved. It is only to select the right partition at all distributed databases.
  • FIG 13-16 describes this through sequence diagrams. It is however possible to avoid duplicate storage of redundant information by using references from each database partition to gambler specific data instead of storing the same data at several partitions.
  • figure 6 there is disclosed a table illustrating the prognosis calculation The prognosis calculation compares the progress between every pair of time controls for the actual athlete (B in this example) and the athlete/athletes that has passed the time control for which the prognosis is calculated (time control 6 - the Goal which only athlete A has passed in this example). Each of the comparison results is multiplied with a weight factor k(,).
  • the comparison results represents the actual tendency between actual athlete (B in this example) and the athlete/athletes passed next time control (A in this example) for every pair of time controls passed so far.
  • the correlation is calculated for each pair of part time controls both A and B in this example has passed.
  • the algorithm makes it possible to give higher priority to the correlation between the athletes at the later part of the race.
  • the algorithm estimate the time for athlete B from time control 5 to the goal by a calculation assuming the tendency between the athletes progress will remain during the last part of the race.
  • FIG 3 there is disclosed a schematic diagram of some computer program products according to the present invention.
  • n different digital computers 100 ⁇ , ..., 100 n wherein n is an integer.
  • n different computer program products 102* ⁇ , ..., 102 n here shown in the form of compact discs.
  • the different computer program products 102- 1 , ..., 102 n are directly loadable into the internal memory of the n different digital computers 100 ⁇ , ..., 100 n .
  • Each computer program product 102 ⁇ , ..., 102 n comprises software code portions for performing some or all the steps of figure 2 when the product(s) 102- 1 , ..., 102 n is/are run on said computer(s) 100 ⁇ , ..., 100 n .
  • Said computer program products 102 ⁇ , ..., 102 n can e. g. be in the form of floppy disks, RAM disks, magnetic tapes, opto magnetical disks or ay other suitable products.
  • the invention is not limited to the embodiments described in the foregoing. It will be obvious that many different modifications are possible within the scope of the following claims.

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Abstract

L'invention se rapporte à un système permettant de gestionner et de fournir des résultats, des pronostics et des informations de jeux et paris en temps réel pour des événements sportifs géographiquement dispersés. Ce système comprend plusieurs dispositifs de communication sans fil, chacun étant identifié au moyen d'un code d'identification unique. Au moins un de ces dispositifs est situé à l'endroit où se déroule chaque événement sportif de façon à émettre les résultats/informations à au moins un élément de commande connecté à un premier système de mémoire servant à stocker ces résultats/informations. Ce système comprend également des moyens de calcul reliés à au moins un desdits éléments de commande et audit système de mémoire. Ces moyens de calcul sont utilisés pour calculer un pronostic, à partir des résultats/informations stockés, à l'aide d'un algorithme itératif et adaptatif. Ce système comprend également un système de mémoire relié aux moyens de calcul et permettant de stocker ces pronostics. Lesdits seconds moyens de mémoire sont connectés à l'élément ou aux éléments de commande qui, eux, émettent ces résultats/informations et/ou pronostiques et/ou demande de conseils aux utilisateurs dudit système par des réseaux mobiles ou fixes.
PCT/SE2003/000516 2002-04-05 2003-03-31 Systeme utilisable pour gestionner et fournir des resultats, des pronostics et des informations de jeux et paris pour des evenements sportifs geographiquement disperses Ceased WO2003088584A1 (fr)

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US7934983B1 (en) 2009-11-24 2011-05-03 Seth Eisner Location-aware distributed sporting events
WO2011088676A1 (fr) * 2010-01-21 2011-07-28 中兴通讯股份有限公司 Procédé de commande à distance d'appareil électroménager et carte de réseau sans fil
US9757639B2 (en) 2009-11-24 2017-09-12 Seth E. Eisner Trust Disparity correction for location-aware distributed sporting events

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7934983B1 (en) 2009-11-24 2011-05-03 Seth Eisner Location-aware distributed sporting events
US8333643B2 (en) 2009-11-24 2012-12-18 Seth Eisner Location-aware distributed sporting events
US8897903B2 (en) 2009-11-24 2014-11-25 Seth Eisner Location-aware distributed sporting events
US9757639B2 (en) 2009-11-24 2017-09-12 Seth E. Eisner Trust Disparity correction for location-aware distributed sporting events
US10092812B2 (en) 2009-11-24 2018-10-09 Seth E. Eisner Trust Disparity correction for location-aware distributed sporting events
WO2011088676A1 (fr) * 2010-01-21 2011-07-28 中兴通讯股份有限公司 Procédé de commande à distance d'appareil électroménager et carte de réseau sans fil
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