Notice bibliographique
Résumé
Realizing a Kid's Dream of Computerized Baseball Gary Cokins (bio) Growing up in the 1950s, we did not have PlayStations, Nintendos, or Xboxes—no video games at all. We did not have 150 channels on cable or satellite TV or VHS movies or DVDs. We had no surround sound or CDs, no cell phones, no personal computers, and no Internet. But we did have our imaginations, and we made up games. One game I played was dice baseball. In college in 1970, I programmed a baseball computer game based on my dice baseball game that has been accepted in the Baseball Hall of Fame as the oldest baseball game computer code. How did this journey happen? When I was about ten years old, my cousin George introduced me to a game that involved rolling two dice; their combinations when tossed equated to hits, doubles, homers, or outs. I will always remember the dice combinations: two is a home run, three a double, four an out, and so on through twelve. The probabilities of the dice combinations with hits were comparable to a typical player's season batting average. In my junior high school days, I played this game hundreds of times and began keeping cumulative statistics for batter and team records. I played a full thirty-two-game season with all the National League teams, and I even played an All-Star game after each team had played sixteen games by selecting the batters and pitchers with the best individual records. I am convinced that the skills I learned from adding up batter's records and calculating batting averages from my simple game prepared me for a business career that involves measures and metrics. It led to my authoring books and presenting seminars in over thirty international cities annually on how organizations can improve their performance. As a manager of performance management solutions at SAS, a world leader in business intelligence and analytics software, I observe incredible computer applications, ranging from biotech industry drug testing analysis to retailers forecasting line-item [End Page 147] inventory for tens of thousands of shelf-items in hundreds, sometimes thousands, of stores. From Child's Play to Game Theory During my junior year at Cornell University, I took a game theory course taught by Henry David Block, a famous professor who wrote computer chess games that played against computerized chess games written by Russian professors. I majored in industrial engineering and operations research, which was a demanding curriculum on top of being a varsity football letterman. Professor Block, with his classic white Santa Claus beard, was an inspiration to me. His course, titled Bionics and Robots, was fun and less stressful than other engineering courses. For my term project in this course, I wrote a computer program using a programming language that was a blend of COBOL and FORTRAN—two "ancient" programming languages. The computer code applied a random number generator (in place of the two dice) for one hundred possible outcomes of batter hits that simulated every batter's at-bat for the 1969 National League season. I still have the original hard-copy print-out on that big paper with the paper-feed holes—a true relic of the past. It was programmed on IBM punch cards that you had to walk to the data center to submit your card deck. This program's deck of punch cards was a foot thick. My classmate, Pete Watzka (who, coincidently, led the 1971 Ivy League baseball conference in batting average), calibrated each player to his 1969 batting average. Our computer program also differentiated batting power between single and home-run hitters, and for each game we adjusted the players "down or up" based on the quality of the opposing pitcher's 1969 record. Our computer program simulated all of the games played for each National League team. Pete and I enjoyed changing the input variables of players and pitchers and seeing how the outcomes changed each time we processed a computer run. On May 13, 1970, Pete and I submitted the "final" computer run for the course paper. The fun outcome of that run was that the team's win-loss percentage records...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».