Derbyshire Senior Cup stats & predictions
Expert Betting Preview: Derbyshire Senior Cup
The Derbyshire Senior Cup, a storied competition in England, is renowned for its rich history and competitive spirit. As the season progresses, teams are vying for supremacy, making this an exciting period for bettors. The league's structure and the unpredictable nature of knockout football offer numerous betting opportunities. This preview provides an in-depth analysis of the current season, featuring daily match predictions, league statistics, and expert betting advice to help you make informed wagers.
No football matches found matching your criteria.
Comprehensive League Overview
This season's Derbyshire Senior Cup has seen a mix of traditional powerhouses and emerging contenders. With 32 teams competing in a knockout format, each match carries significant weight, influencing both the league standings and future matchups. The betting landscape is dynamic, with odds reflecting not only team form but also historical performance and managerial tactics.
Current Season Context
The current campaign has been marked by unexpected results and thrilling encounters. Teams like Chesterfield FC have shown resilience, while underdogs such as Matlock Town have made surprising progress. This unpredictability makes the tournament a fertile ground for strategic betting.
Betting Landscape
- The odds market is highly volatile due to the knockout nature of the competition.
- Bettors should focus on value bets where strong teams face weaker opponents.
- Over/under goals markets are particularly lucrative given the open style of play.
Daily Match Predictions
Featured Matches
Today's matches feature several key clashes that could determine the trajectory of the tournament. Highlighting these fixtures provides insight into potential betting opportunities.
Match 1: Chesterfield FC vs Matlock Town
Chesterfield FC enters this match as favorites due to their superior squad depth and recent form. However, Matlock Town's defensive solidity makes them a formidable opponent. A draw could be a prudent bet given their ability to frustrate stronger teams.
- Prediction: Draw (1-1)
- Betting Tip: Over 2.5 goals at +150 odds
Match 2: Alfreton Town vs Ilkeston Town
Alfreton Town has been in excellent form, showcasing attacking prowess that could trouble Ilkeston's defense. However, Ilkeston's counter-attacking strategy may exploit any gaps left by Alfreton's high press.
- Prediction: Alfreton Town to win (1-0)
- Betting Tip: Correct score at +200 odds
Betting Opportunities
The knockout format ensures that every game is crucial, offering multiple betting angles such as first goal scorer or total corners scored.
- Focusing on first-half goals can yield profitable outcomes due to early momentum shifts.
- Total corners can be indicative of possession battles in tightly contested matches.
League Statistics Analysis
Team Performance Metrics
Analyzing team performance metrics provides deeper insights into potential outcomes. Key statistics include average goals scored per match, defensive records, and head-to-head results.
- Chesterfield FC averages 1.8 goals per game with a strong home record.
- MATLOCK TOWN has conceded fewer than two goals in their last five matches.
Trends and Patterns
Trends such as home advantage and recent form are critical when assessing upcoming matches. Teams with positive momentum often carry their confidence into subsequent games.
- Home teams have won 60% of their matches this season.
- Last-minute winners are more common than expected due to high-pressure situations in knockout rounds.
Key Metrics Analysis
Evaluating key metrics like shots on target and possession percentage helps predict match dynamics:
- A higher shots-on-target ratio often correlates with winning margins in closely contested games.
- Possession stats can be misleading; efficient counter-attacks often outperform possession-heavy tactics in knockout stages.
Expert Betting Advice
Betting Strategies for Success
To maximize returns from betting on the Derbyshire Senior Cup, employing well-thought-out strategies is essential:
- Diversify your bets across different markets to mitigate risk while capitalizing on varied opportunities.
- Focusing on value bets rather than outright favorites increases long-term profitability.
Focusing on Underdog Value Bets
Sometimes lesser-known teams offer substantial value against stronger opponents due to underestimated capabilities or tactical surprises.
- Analyze past performances against similar caliber opponents.
Leveraging Statistical Models
Incorporating statistical models into your betting approach allows for data-driven decisions based on historical trends.
- Data analytics tools can uncover hidden patterns not immediately apparent through casual observation.
Tips for Bet Placing Timing
The timing of placing bets can significantly impact potential returns:
<|repo_name|>jamesmcnamara12/ai-instructions<|file_sep|>/treebank/treebanks/fr_gsd/fr_gsd-pos-AUX.md
---
layout: base
title: 'Statistics of AUX in UD_French-GSD'
udver: '2'
---
## Treebank Statistics: UD_French-GSD: POS Tags: `AUX`
There are 11 `AUX` lemmas (0%), 43 `AUX` types (0%) and 13061 `AUX` tokens (7%).
Out of 17 observed tags, the rank of `AUX` is: 8 in number of lemmas, 9 in number of types and 5 in number of tokens.
The 10 most frequent `AUX` lemmas: _être_, _avoir_, _devoir_, _aller_, _vouloir_, _falloir_, _faire_, _savoir_, _venir_, _peut-être_
The 10 most frequent `AUX` types: _est_, _a_, _sont_, _était_, _été_, _avait_, _ont_, _étant_, _avons_, _sera_
The 10 most frequent ambiguous lemmas: _être_ (AUX 7916, VERB 243), _avoir_ (AUX 4739, VERB 219), _devoir_ (AUX 1046, VERB 83), _aller_ (AUX 504, VERB 141), _vouloir_ (AUX 233, VERB 76), _falloir_ (AUX 148, VERB 14), _faire_ (AUX 140, VERB 1119), _savoir_ (AUX 112, VERB#ifndef VEHICLE_H
#define VEHICLE_H
#include "car.h"
#include "bus.h"
#include "truck.h"
class Vehicle {
public:
Vehicle();
~Vehicle();
void setVehicle(Car *c);
void setVehicle(Bus *b);
void setVehicle(Truck *t);
Car *getCar();
Bus *getBus();
Truck *getTruck();
private:
Car *car;
Bus *bus;
Truck *truck;
};
#endif // !VEHICLE_H
<|repo_name|>kobakun/diplo<|file_septorch==1.6
numpy==1.18
scikit-image==0.17
opencv-python==4.5
matplotlib==3.0
pytorch-lightning==0.8
gym>=0,<0.*
gym[atari]
tensorflow-datasets==4.*
tensorboardX>=1,<2.*<|repo_name|>kobakun/diplo<|file_sepled.py -i /dev/ttyUSB0 -r -b57600 --format=hex -d /tmp/log.txt --duration=20 &
#python main.py --policy-file=./models/ppo/policy.pkl --env-name=PongNoFrameskip-v4 --num-envs=16 --seed=42 --log-dir=./logs/ppo_vizdoom
python main.py --policy-file=./models/ppo/policy.pkl --env-name=PongNoFrameskip-v4 --num-envs=16 --seed=42 --log-dir=./logs/ppo_vizdoom &
python main.py
--policy-file=./models/dqn/policy.pkl
--env-name=PongNoFrameskip-v4
--num-envs=16
--seed=42
--log-dir=./logs/dqn_vizdoom
&
python main.py
--policy-file=./models/ddpg/policy.pkl
--env-name=PongNoFrameskip-v4
--num-envs=16
--seed=42
--log-dir=./logs/ddpg_vizdoom
&
python main.py
--policy-file=./models/aacnn/policy.pkl
--env-name=PongNoFrameskip-v4
--num-envs=16
--seed=42
--log-dir=./logs/aacnn_vizdoom
&
<|repo_name|>kobakun/diplo<|file_sep pdflatex report.tex && bibtex report.aux && pdflatex report.tex && pdflatex report.tex <|repo_name|>kobakun/diplo<|file_sep* {
font-family : courier new,courier;
font-size : medium;
}
body {
background-color : #ffffff;
color : #000000;
margin : auto;
width : auto;
max-width : none;
padding : none;
}
div.header {
text-align : center;
padding-top : .5in;
padding-bottom : .5in;
}
div.titlepage {
text-align : center;
}
div.section-title {
font-size : large;
}
div.section-content {
padding-left : .75in;
padding-right : .75in;
}
div.paragraph-title {
font-size : medium;
}
div.paragraph-content {
padding-left : .75in;
padding-right : .75in;
}
table.toc {
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table.toc tr td:first-child:before {
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table.toc tr td:nth-child(even) {
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table.table {
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table.table caption {
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table.table th {
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table.table td {
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div.figure {
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div.figure p.caption {
}<|file_sep[//]
// [Title] Artificial Intelligence Coursework Report
// [Author] Kevin OBrien
// [Date] May/2020
import numpy as np
import torch.nn as nn
import torch.optim as optim
from pytorch_lightning.core.lightning import LightningModule
class Actor(nn.Module):
def __init__(self):
super(Actor,self).__init__()
self.fc = nn.Sequential(
nn.Linear(84*84*4+6 ,256),
nn.ReLU(),
nn.Linear(256 ,256),
nn.ReLU(),
nn.Linear(256 ,6)
)
def forward(self,x):
return self.fc(x)
class Critic(nn.Module):
def __init__(self):
super(Critic,self).__init__()
self.fc = nn.Sequential(
nn.Linear(84*84*4+6 ,256),
nn.ReLU(),
nn.Linear(256 ,256),
nn.ReLU(),
nn.Linear(256 ,1)
)
def forward(self,x):
return self.fc(x)<|repo_name|>kobakun/diplo<|file_sep#include "vehicle.h"
using namespace std;
Vehicle::Vehicle() {}
Vehicle::~Vehicle() {}
void Vehicle::setVehicle(Car *c) { car = c; }
void Vehicle::setVehicle(Bus *b) { bus = b; }
void Vehicle::setVehicle(Truck *t) { truck = t; }
Car *Vehicle::getCar() { return car; }
Bus *Vehicle::getBus() { return bus; }
Truck *Vehicle::getTruck() { return truck; }<|repo_name|>kobakun/diplo<|file_sep germansimulator.cpp
#include "germansimulator.h"
#include "serial_communication.h"
using namespace std;
GermanSimulator::GermanSimulator(string portname,int baudrate):portname(portname),baudrate(baudrate){
port.openPort(portname.c_str(),baudrate);
cout<<"Port Openedn";
}
void GermanSimulator::start(){
port.startCommunication();
cout<<"Startedn";
}
int GermanSimulator::readSensorData(SensorData &data){
return port.readSensorData(data);
}
int GermanSimulator::sendControlData(ControlData data){
return port.sendControlData(data);
}
GermanSimulator::~GermanSimulator(){
port.closePort();
}<|repo_name|>kobakun/diplo<|file_sep Charts.tex
documentclass{article} % For LaTeX2e
usepackage{amsmath} % For mathbb
begin{document}
section{Charts}label{sec_charts}
In this section we will look at some charts showing how our agent performed over time.
We will compare these charts between all three algorithms used.
subsection{Episode Reward}label{subsec_episode_reward}
This chart shows us how much reward our agent was getting per episode.
We will notice that PPO performs better than DQN here.
It seems that DQN struggles early on but then eventually catches up.
This could be because DQN does not use any kind of exploration technique whereas PPO does.
This means PPO will explore more actions early on which gives it an advantage.
begin{figure}[!ht]
centering
begin{minipage}{7cm}
centering
includegraphics[width=linewidth]{images/reward_ppo.png} \
PPO\
Reward per Episode\
Mean $pm$ Std Deviation\
Over Last $100$ Episodes\
Log Scale Y Axis\
Smoothed by $25$ Points\
end{minipage}quad%
begin{minipage}{7cm}
centering
includegraphics[width=linewidth]{images/reward_dqn.png}\
DQN\
Reward per Episode\
Mean $pm$ Std Deviation\
Over Last $100$ Episodes\
Log Scale Y Axis\
Smoothed by $25$ Points\
end{minipage}quad%
%begin{minipage}{7cm}
%centering
%includegraphics[width=linewidth]{images/reward_ddpg.png}\
%DDPG\
%Reward per Episode\
%Mean $pm$ Std Deviation\
%Over Last $100$ Episodes\
%Log Scale Y Axis\
%Smoothed by $25$ Points \
%end{minipage}\
captionof{figure}{Episode Reward Chart for all three algorithms used over training time.label{fig_episode_reward}}
%captionof{}
%captionof{}
%captionof{}
%captionof{}
%captionof{}
vspace{-15pt}
vspace{-15pt}
vspace{-15pt}
vspace{-15pt}
vspace{-15pt}
vspace{-15pt}
vspace{-15pt}
vspace{-15pt}
vspace{-15pt}
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vspace{-15pt}
vspace{-15pt}
vspace{-15pt}
vspace{-20pt}
vfill
fbox{parbox{textwidth}{
This section shows how many rewards our agent got over each episode during training.
We see that PPO performs best here.
It also seems that PPO learns faster than DQN because it gets more rewards earlier.
This means it will take less time overall to train than DQN.
DDPG performs worst here because it gets stuck doing suboptimal actions very early on during training.
DDPG cannot seem to recover from this mistake even though it is using an exploration technique called Ornstein-Uhlenbeck noise.newline
}}
%newpage
%vfill
%newpage
%newpage
end{document}<|repo_name|>kobakun/diplo<|file_sep founding experiments.tex
The first thing we need to do before we begin developing our own model is establish some baseline results so we know what performance we should expect from our model.
For these baseline experiments we will use pre-trained models available through PyTorch Lightning which I previously mentioned.
I chose these three algorithms because they all represent different approaches towards solving reinforcement learning problems.
PPO uses policy gradients whereas DDQN uses Q-learning so they represent two different approaches towards solving reinforcement learning problems.
Finally I chose AANet because it is designed specifically for playing Atari games so I thought it would be interesting if it performed well compared to other algorithms.
Firstly lets look at PPO:
We trained PPO using a custom environment built from VizDoom with screen size $(84times84)$ using PyTorch Lightning.
The hyperparameters used were:
$gamma =0.99$
$lambda =0$
$n_{steps}=2048$
$n_{epochs}=10$
$n_{batch}=32$
$v_coef=0$
$epsilon =0$
$r_clip=10$
Using these hyperparameters we were able to achieve a score greater than $50000$. Figure~(ref{fig_ppo_performance}) shows how well PPO performed during training.
Now lets look at DDQN:
We trained DDQN using exactly same environment as above but with screen size $(64times64)$ using PyTorch Lightning.
The hyperparameters used were:
$gamma =0.99$
$epsilon_start =1$
$epsilon_end =0.01$
$epsilon_decay=$steps$/25000000$
$alpha =$lr$
$
=
$$lr_start=$lr$
$
=
$$lr_end=$lr$
$
=
$$lr_decay=$steps$/25000000$
$n_{target}$swap =$10000$
Using these hyperparameters we were able to achieve a score greater than $50000$. Figure~(ref{fig_ddqn_performance}) shows how well DDQN performed during training.
Finally lets look at AANet:
We trained AANet using exactly same environment as above but with screen size $(80times80)$ using PyTorch Lightning.
Using default hyperparameters we were able to achieve a score greater than $50000$. Figure~(ref{fig_aanet_performance}) shows how well AANet performed during training.
These results show us what kind of performance we should expect from our model.
bibliographystyle{siam.bst} %Siam style bibliography file provided by SUTD library.
bibliography{siambib.bib}
nocite{*}<|repo_name|>/home/koba/Documents/github-repos/diploma-project/bib/siambib.bib
