FA Cup Extra Preliminary Round stats & predictions

The Thrill of the FA Cup: Extra Preliminary Round England

The FA Cup, often referred to as the 'Greatest Show on Grass', is a tournament steeped in history and tradition. As we dive into the Extra Preliminary Round of the English FA Cup, excitement builds across the nation. This round marks the beginning of a journey where dreams are made and legends are born. With fresh matches updating daily, fans are treated to a spectacle of skill, passion, and unpredictability. This year's round promises thrilling encounters, as lower league teams take on higher-ranked opponents in a quest for glory.

Understanding the Extra Preliminary Round

The Extra Preliminary Round is the first official stage of the FA Cup competition. It features a mix of non-league clubs and some lower-tier professional teams. The winners of this round will advance to the First Qualifying Round, continuing their pursuit of a place in the prestigious knockout stages. This round is particularly exciting because it embodies the essence of the FA Cup – any team can triumph against another, regardless of their league status.

Key Matches to Watch

As we approach each matchday, certain fixtures stand out due to their potential for upsets or standout performances. Here are some key matches that football enthusiasts should keep an eye on:

• Non-League Giants vs. Lower League Teams: Matches featuring non-league giants like AFC Totton or Bromley against lower league sides often result in memorable battles. These games highlight the unpredictability and drama that make the FA Cup so beloved.
• Local Rivalries: Encounters between local rivals can be particularly intense, with added pressure and passion fueling both sides. Fans look forward to these clashes, knowing they will witness high-energy performances.
• Highly Talented Youths: Some matches feature young talents who are eager to make their mark on the big stage. These players bring fresh energy and creativity, often changing the dynamics of a game.

Betting Predictions: Expert Insights

Betting on FA Cup matches adds an extra layer of excitement for fans. With expert predictions available daily, punters can make informed decisions and potentially reap significant rewards. Here are some insights from our experts:

Factors Influencing Predictions

• Team Form: Current form is a crucial factor. Teams on a winning streak or those showing significant improvement are more likely to perform well.
• Injuries and Suspensions: Key player absences can drastically affect a team's chances. Keeping an eye on injury reports is essential for accurate predictions.
• Home Advantage: Playing at home often provides teams with a boost in morale and performance. This factor is particularly important in close matches.

Tips for Betting Success

• Diversify Bets: Spread your bets across different types of outcomes (e.g., win, draw, under/over goals) to increase your chances of success.
• Analyze Head-to-Head Records: Historical data between teams can provide valuable insights into potential match outcomes.
• Follow Expert Opinions: Stay updated with expert analyses and predictions to refine your betting strategies.

Daily Match Updates: Staying Informed

To keep up with the latest developments in the Extra Preliminary Round, fans can rely on daily updates from trusted sources. These updates provide comprehensive coverage of each match, including pre-match analysis, live commentary, and post-match reports.

How to Access Daily Updates

• Social Media Platforms: Follow official FA Cup pages and reputable sports news outlets on social media for real-time updates and highlights.
• Email Newsletters: Subscribe to newsletters from football websites to receive daily summaries and expert insights directly in your inbox.
• Sports Apps: Download dedicated sports apps that offer live scores, match notifications, and detailed statistics.

The Magic of Underdog Stories

The FA Cup is renowned for its underdog stories – tales of smaller clubs defeating giants against all odds. These stories capture the imagination of fans worldwide and are a testament to the unpredictable nature of football. Here are some memorable underdog triumphs from previous years:

• Gillingham's Historic Victory (1982): Gillingham became one of only two non-league teams to win an FA Cup tie against a top-flight team when they defeated Manchester United at Old Trafford.
• Leyton Orient's Fairytale Run (2002): Leyton Orient reached the quarter-finals as non-league minnows, defeating several higher-ranked teams along the way before being eliminated by Arsenal.
• Plymouth Argyle's Sensational Win (2006): Plymouth Argyle stunned Manchester City with a 2-1 victory at Home Park, showcasing their determination and skill.

Celebrating Local Heroes: Player Performances to Watch

In every round of the FA Cup, certain players emerge as local heroes, delivering standout performances that capture the hearts of fans. Here are some players to watch during this season's Extra Preliminary Round:

• Junior Forwards Making Their Mark: Keep an eye on young forwards eager to prove themselves on a big stage. Their pace and creativity can be game-changers.
• Veteran Midfield Maestros: Experienced midfielders bring leadership and composure to their teams, often dictating the tempo of a match with their vision and passing ability.
• Inspiring Goalkeepers: Goalkeepers who make crucial saves or contribute offensively can turn the tide in tight contests.

The Role of Community Support: Fans' Impact on Matches

Fans play an integral role in shaping the atmosphere and outcome of FA Cup matches. The support from local communities can provide teams with an invaluable boost, especially when facing stronger opponents. Here’s how fan support influences matches:

• Motivational Boost for Players: The presence of passionate supporters can inspire players to perform at their best, pushing them beyond their limits.
• Create Intimidating Atmosphere for Opponents: A vocal crowd can unsettle visiting teams, making it harder for them to execute their game plan effectively.
• Celebration of Local Pride: Matches become community events where fans unite to celebrate their town or city’s participation in a historic competition.

Tactical Analysis: How Teams Prepare for Their Opponents

Tactical preparation is crucial for teams competing in the Extra Preliminary Round. Coaches analyze opponents’ strengths and weaknesses to devise effective strategies. Here’s what goes into tactical planning:

• Analyzing Opposition Play Style: Understanding whether an opponent favors attacking play or adopts a defensive approach helps teams tailor their tactics accordingly.
• Focusing on Key Players: Identifying and neutralizing key threats from opposition sides is essential for gaining an upper hand during matches.
• Possession-Based Strategies vs Counter-Attacking Approaches: Teams may choose between maintaining possession or exploiting spaces through quick counter-attacks based on their assessment of opponents’ vulnerabilities.

Tactical Trends Observed This Season

• Rise in High-Pressing Systems: More teams are adopting high-pressing tactics early in matches to disrupt opponents’ build-up play and create scoring opportunities quickly.
• mashov21/Bachelor-Thesis<|file_sep|>/chapters/chapter1.tex chapter{Introduction} label{cha:introduction} In recent years there has been growing interest in mobile robots that assist humans during everyday tasks cite{Schoellig2016}. The most common way robots assist humans is by providing physical support cite{Hoffman2008}. An example would be robots that support people during rehabilitation cite{Buechel2017}, people who need help getting dressed cite{Matsushita2017} or robots that support people with limited mobility cite{Yang2017}. Although physical assistance is very helpful it also has several disadvantages such as not being able to be used outside hospitals cite{Hoffman2008}. Another way robots assist humans is by providing cognitive assistance cite{Schoellig2016}. Cognitive assistance means that instead of physically supporting humans during everyday tasks robots provide information about these tasks cite{Schoellig2016}. Cognitive assistance can be used by almost everyone because it does not require any physical interaction between humans and robots cite{Hoffman2008}. Furthermore cognitive assistance can be used outside hospitals which makes it much more practical than physical assistance cite{Hoffman2008}. An example for cognitive assistance is when robots assist humans during cooking cite{Ahn2015}. Cooking consists out of multiple tasks such as washing ingredients before use or heating water before using it cite{Ahn2015}. A robot can assist humans by providing information about which ingredients need washing before use or how long water needs heating before it can be used cite{Ahn2015}. Another example would be robots that assist users when cooking by providing information about how long food needs cooking until it is ready cite{Ahn2015}. In this thesis we will focus on assisting users during cooking by providing information about which ingredients need washing before use cite{Ahn2015}. To achieve this goal we will use machine learning techniques such as convolutional neural networks (CNNs) cite{LeCun1998}. This thesis consists out of 7 chapters: begin{itemize} item Chapter~ref{cha:introduction} describes our motivation for this thesis as well as our problem statement. item Chapter~ref{cha:related_work} describes related work. item Chapter~ref{cha:theory} describes theory related work. item Chapter~ref{cha:dataset} describes our dataset. item Chapter~ref{cha:methodology} describes our methodology. item Chapter~ref{cha:results} describes our results. item Chapter~ref{cha:conclusion} describes our conclusions. end{itemize} <|file_sep|>chapter*{centering Abstract} addcontentsline{toc}{chapter}{Abstract} The goal of this thesis is to develop an algorithm that uses machine learning techniques such as convolutional neural networks (CNNs) cite{LeCun1998} to detect food items from images captured by users with smartphones. Our motivation for this thesis comes from research conducted by Ahn et al. where they developed a system that assists users during cooking by providing information about which ingredients need washing before use cite{Ahn2015}. We aim at building upon this work by creating an algorithm that detects food items from images captured by users with smartphones. Our dataset consists out of $75$ images taken from $9$ different categories $-$ beans, cabbage leaves, carrots, garlic cloves, ginger roots, green onions, mushrooms slices, pepper slices and tomato slices $-$ resulting in approximately $8$ images per category. We trained $13$ different CNN models with $4$ different optimizers each resulting in $52$ trained models which we evaluated using metrics such as accuracy (ACC), precision (PRE), recall (REC), F1-score (F1) and intersection over union (IOU). We found out that models using Adam optimizer achieved better results than models using SGD optimizer while using learning rate decay did not improve model performance. The best performing model achieved ACC = 0.92 $pm$ 0.02 PRE = 0.93 $pm$ 0.01 REC = 0.91 $pm$ 0.02 F1 = 0.92 $pm$ 0.01 IOU = 0.88 $pm$ 0.02. <|repo_name|>mashov21/Bachelor-Thesis<|file_sep|>/chapters/chapter6.tex chapter*{centering Conclusion} addcontentsline{toc}{chapter}{Conclusion} We have developed an algorithm that detects food items from images captured by users with smartphones using machine learning techniques such as convolutional neural networks (CNNs) cite{LeCun1998}. We created a dataset consisting out of $75$ images taken from $9$ different categories $-$ beans ($8$ images), cabbage leaves ($8$ images), carrots ($8$ images), garlic cloves ($8$ images), ginger roots ($7$ images), green onions ($8$ images), mushrooms slices ($7$ images), pepper slices ($9$ images) and tomato slices ($8$ images). We trained $13$ different CNN models with $4$ different optimizers each resulting in $52$ trained models which we evaluated using metrics such as accuracy (ACC), precision (PRE), recall (REC), F1-score (F1) and intersection over union (IOU). We found out that models using Adam optimizer achieved better results than models using SGD optimizer while using learning rate decay did not improve model performance. The best performing model achieved ACC = 0.92 $pm$ 0.02 PRE = 0.93 $pm$ 0.01 REC = 0.91 $pm$ 0.02 F1 = 0.92 $pm$ 0.01 IOU = 0.88 $pm$ 0.02. <|repo_name|>mashov21/Bachelor-Thesis<|file_sep|>/bib.bib @article{khan2019food, title={Food Image Classification Using Deep Learning}, author={Khan ZH et al}, journal={International Journal on Smart Sensing Systems}, volume={6}, number={2}, pages={103--116}, year={2019} } @article{khan2021food, title={Food Image Classification Using Deep Learning}, author={Khan ZH et al}, journal={International Journal on Smart Sensing Systems}, volume={6}, number={2}, pages={103--116}, year={2021} } @inproceedings{schoellig2016cognitive, title={Cognitive assistance systems for elderly care}, author={Schoellig, Alexander P et al}, booktitle={Proceedings - IEEE International Conference on Robotics and Automation Workshops}, pages={243--248}, year={2016} } @inproceedings{schoellig2021cognitive, title={Cognitive Assistance Systems for Elderly Care}, author={Schoellig Alexander P et al}, editor={IEEE International Conference on Robotics and Automation Workshops}, volume={2021-August}, number={33}, pages={243--248}, year={2021} } @article{schoellig2021cognitivea, title={Cognitive Assistance Systems for Elderly Care}, author={Schoellig Alexander P et al}, journal={IEEE International Conference on Robotics and Automation Workshops}, volume={33}, number={243--248}, pages={August}, year={2021} } @article{khan2022food, title={Food Image Classification Using Deep Learning}, author={Khan ZH et al}, journal={International Journal On Smart Sensing Systems }, volume={6}, number={2}, pages={103--116}, doi = {10.1007/s13218-019-00555-5} } @inproceedings{khan2022fooda, title={{Food Image Classification Using Deep Learning}}, author={{Khan ZH et al}}, editor={{International Journal On Smart Sensing Systems}}, volume={{6}}, number={{2}}, pages={{103–116}}, doi={{10.1007/s13218-019-00555-5}} } @inproceedings{khan2022foodb, title={{Food Image Classification Using Deep Learning}}, author={{Khan ZH et al}}, editor={{International Journal On Smart Sensing Systems}}, volume={{6}}, number={{2}}, pages={{103–116}}, doi={{10://doi.org/10/1007/s13218-019-00555-5}} } @inproceedings{khan2022fooda, title={{Food Image Classification Using Deep Learning}}, author={{Khan ZH et al}}, editor={{International Journal On Smart Sensing Systems}}, volume={{6}}, number={{2}}, pages={{103–116}}, doi={{https://doi.org/10/1007/s13218-019-00555-5}} } @article{khan2022foodc, title={{Food Image Classification Using Deep Learning}}, author={{Khan ZH et al}}, journal={{International Journal On Smart Sensing Systems }}, volume={{6}}, number={{2}}, pages={{103–116}}, doi={{https://doi.org/10/1007/s13218-019-00555-5}} } @inproceedings{khan2022foodd, title={{Food Image Classification Using Deep Learning}}, author={{Khan ZH et al}}, editor={{International Journal On Smart Sensing Systems }}, volume= {{6}}, number= {{2}}, pages= {{103–116}}, doi= {{https://doi.org/10/1007/s13218-019-00555-5}} } @article{khan2022foode, title= {{Food Image Classification Using Deep Learning }}, author= {{Khan ZH et al }}, journal= {{International Journal On Smart Sensing Systems }}, volume= {{6 }}, number= {{2 }}, pages= {{103–116 }}, doi= {{https://doi.org/10/1007/s13218-019-00555-5 }} } @article{khan2022foodf, title= {{Food Image Classification Using Deep Learning }}, author= {Khan ZH et al }, journal= {International Journal On Smart Sensing Systems }, volume= {6}, number= {2}, pages= {103–116}, doi= {https://doi.org/