AUTOR IZ DAVNOG VREMENA KADA JE PISAO SVOJU PRVU KNJIGU „KRILATA KATEDRA”...
Poput mnogih drugih, tako je i Zoran Modli rođen sredinom prošlog veka u Zemunu i za sada je živ i zdrav. Nije odmah postao pilot. Najpre je kao odlikaš završio osnovnu školu, a onda alarmantno srozao uspeh u Prvoj zemunskoj gimnaziji. Od mature se oporavio u redakciji „Politike ekspres”, a sa dvadesetak godina proslavio kao revolucionarni disk-džokej Studija B i legendarne zemunske diskoteke „Sinagoga”. Studio B je, posle pet godina, napustio iz više razloga, a najviše zbog letenja. Od tada je jednom nogom u raznim radijima, a drugom i obema rukama u avijaciji. Pošto je bliska rodbina, a naročito najbliža – majka – očekivala da završi kakav-takav fakultet, uradio je pola posla, pa završio Višu vazduhoplovnu pilotsku školu u Beogradu.
Kao instruktor letenja, najpre na sportskim aerodromima, a zatim u Pilotskoj akademiji JAT u Vršcu, školovao je na desetine naših i stranih pilota. Mnogi od njih odavno su kapetani JAT-a, ali i drugih kompanija širom sveta. Dvadeset godina je leteo u JAT-u, a najviše vremena proveo na nikad prežaljenom boingu 727, nad kojim lamentira kad god mu se za to pruži prilika. Od ranih devedesetih pa sve do prvog poglavlja ove knjige leteo je i kao kapetan na biznis-džetovima kompanije Prince Aviation. Za njim su bezbrojni sati sjajnih iskustava. Poslednje je bilo loše, ali korisno za ovu knjigu.
Živi u Beogradu, a u mislima u svim onim gradovima na čije je aerodrome sletao.
... I U OVA NOVA VREMENA, DOK OČEKUJE NOVO IZDANJE „PILOTSKE KNJIGE“.
Abstract The FIFA World Cup is one of the most watched and data‑rich sporting events in the world. Despite its popularity, high‑quality, structured, and reproducible data on every match, goal, card, and player appearance have historically been scattered across multiple sources. The Jfjelstul World Cup database (Jfjelstul, 2022) fills this gap by providing a complete, open‑source, and rigorously curated dataset covering every World Cup edition from 1930 to 2022. This paper details the database’s structure, installation methods (including direct download and R package integration), key variables, and practical applications. We also discuss data validation, limitations, and potential extensions for social scientists, sports analysts, and data science educators. 1. Introduction Since its inception in 1930, the FIFA World Cup has grown into a global phenomenon. Analysts increasingly use historical tournament data to study performance patterns, referee bias, team strategy evolution, and even economic or political impacts on sporting outcomes. However, obtaining reliable, machine‑readable data for all matches, goals, penalties, and disciplinary actions has been a persistent challenge. Many online sources contain errors, missing records, or inconsistent formatting across years.
The package also includes built‑in documentation ( ?matches , ?worldcup ). Python users can read CSVs directly from the raw GitHub URLs: jfjelstul worldcup data download
# Install from CRAN install.packages("worldcup") library(worldcup) See all available data frames data(package = "worldcup") Load specific tables into environment data("matches") data("goals") data("cards") Abstract The FIFA World Cup is one of
library(dplyr) goals_per_match <- matches %>% group_by(year) %>% summarise(avg_goals = mean(home_team_goals + away_team_goals)) Results show a decline from ~4 goals/match in 1930s–50s to ~2.5 goals/match in recent decades, reflecting tactical shifts. Analyze which round produces most red cards: Introduction Since its inception in 1930, the FIFA
