Everton FC: Premier League Squad, Achievements & Stats

Overview of Everton Football Club

Everton Football Club, commonly known as Everton, is a professional football team based in Liverpool, England. The club competes in the Premier League, which is the top tier of English football. Founded in 1878, Everton is one of the oldest and most storied clubs in English football history. The team currently plays under the management of Sean Dyche.

Team History and Achievements

Everton has a rich history with numerous achievements. They have won the English league title 9 times and secured 5 FA Cup victories. Notable seasons include their first league title win in 1890-91 and their recent Premier League triumphs in the late 1980s. Everton has consistently been a competitive side, often finishing among the top teams in the league.

Current Squad and Key Players

The current squad boasts several key players who play pivotal roles. Richarlison, a forward known for his goal-scoring prowess, and Allan, a defensive midfielder renowned for his ball distribution skills, are standout performers. Other notable players include goalkeeper Jordan Pickford and defender Lucas Digne.

Team Playing Style and Tactics

Everton typically employs a balanced playing style with an emphasis on solid defense and quick counter-attacks. Their formation often revolves around a 4-3-3 setup that allows flexibility in both attack and defense. Strengths include strong set-piece performance and resilience under pressure, while weaknesses can be inconsistent form away from home.

Interesting Facts and Unique Traits

Everton’s nickname is “The Toffees,” derived from their original nickname “The Schoolboys” due to their origins at St John’s College School. They have a passionate fanbase known as “The Blues,” reflecting their traditional blue kit color. Rivalries are intense, particularly with nearby Liverpool FC, while traditions like pre-match singing add to the club’s unique culture.

Lists & Rankings of Players & Performance Metrics

• Top Performers:
• Richarlison – Forward (⭐️)
• Allan – Midfielder (⭐️)
• Jordan Pickford – Goalkeeper (🎰)
• Awards:
• PFA Team of the Year Member – Allan (✅)
• FIFA World Cup Winner – Jordan Pickford (🎰)

Comparisons with Other Teams

In comparison to other Premier League teams, Everton stands out for its strong defensive record despite budget constraints relative to top-tier clubs like Manchester City or Liverpool FC. Their ability to compete against financially stronger teams showcases strategic managerial decisions by Sean Dyche.

Case Studies or Notable Matches

A breakthrough game was their victory against Manchester United at Old Trafford during the 2018-19 season which marked a significant point in their campaign that season. Another key victory was securing promotion back to the Premier League after winning against Rotherham United in May 2020.

Team Stats Summary
Premier League Position:	12th (2023)
Last 5 Games Form:	W-D-L-W-L
Head-to-Head Record vs Liverpool:	D-W-L-D-W (Last 5 matches)

Tips & Recommendations for Betting Analysis

• Analyze home vs away performance: Everton often performs better at home.
• Bet on defensive solidity: With strong defensive tactics, they can be tough opponents.
• Leverage player form: Key players like Richarlison can influence match outcomes significantly.

Frequently Asked Questions about Betting on Everton

What are Everton’s strengths when betting?

Everton’s strengths lie in their solid defense and ability to perform well at Goodison Park against top-tier teams.

Are there any weaknesses I should consider?

A potential weakness is inconsistency in away games which can affect betting odds negatively.

How should I analyze upcoming matches?

Analyze recent form, head-to-head records against opponents, and consider player availability due to injuries or suspensions.

Pros & Cons of Current Form or Performance

• Pros:
• Solid defensive structure (✅)
• Tactical adaptability under manager Sean Dyche (✅)
• Cons:
• Inconsistent away performances (❌)</l[0]: #!/usr/bin/env python

  [1]: """
  [2]: This module contains all functions related to training.
  [3]: """

  [4]: import numpy as np
  [5]: import os
  [6]: import tensorflow as tf
  [7]: from tqdm import tqdm

  [8]: from .data import Data

  [9]: def train(model,
  [10]: data,
  [11]: learning_rate=0.001,
  [12]: epochs=10,
  [13]: batch_size=32,
  [14]: num_workers=4,
  [15]: device='cpu',
  [16]: save_path=None):

  [17]: """Trains model on data.

  [18]: Parameters
  [19]: ———-

  [20]: model : tf.keras.Model
  [21]: Model object.

  [22]: data : Data
  [23]: Data object containing training data.

  [24]: learning_rate : float
  [25]: Learning rate used for optimization.

  [26]: epochs : int
  [27]: Number of epochs used for training.

  Number of workers used

  for parallel processing.

  Device used for training.

  Path where model will be saved.

  Batch size used

  for training.

  Training loss array

  Validation loss array

  Optimizer object

  Loss function object

  Training loop.

  Check if GPU is available

  if specified device is 'gpu'.

  If GPU is available

  use it as device

  else use CPU as device.

  Initialize optimizer using Adam algorithm

  with specified learning rate

  .

  Initialize loss function using mean squared error

  function

  .

  Create empty arrays

  with length equal to number of epochs

  for storing losses during training process.

  Loop over epochs

  .

  Print epoch number

  Print percentage progress within epoch

  Create batches

  with size equal to batch size

  from shuffled indices of input data

  .

  Loop over batches

  within epoch

  .

  Get input data batch

  corresponding to current batch index

  .

  Get target data batch

  corresponding to current batch index

  .

  Perform gradient descent step

  using current input data batch

  , target data batch

  , optimizer

  , loss function

  , specified device

  .

  Calculate mean squared error between predictions made by model using input data batch

  ,

  targets corresponding to input data batch

  ,

  then append this value

  to list containing losses calculated during current epoch

  .

  Append mean value of losses calculated during current epoch

  ,

  rounded up to two decimal places

  ,

  to array containing losses calculated during all epochs

  """

  def predict(model,
  inputs,
  targets=None):

  mohamedahmedelmasry/Smart-Water-Pipeline-Monitoring/main.py
  #!/usr/bin/env python

  “””
  This module contains main method that runs program.

  “””

  from argparse import ArgumentParser

  import numpy as np
  import pandas as pd

  from pipeline_monitoring.data import Data
  from pipeline_monitoring.model import Model
  from pipeline_monitoring.training import train

  def main():

  “””Main method that runs program.”””

  # Set argument parser options.

  parser = ArgumentParser()

  parser.add_argument(
  ‘-d’,
  ‘–data_dir’,
  type=str,
  required=True)

  parser.add_argument(
  ‘-m’,
  ‘–model_dir’,
  type=str)

  parser.add_argument(
  ‘-l’,
  ‘–learning_rate’,
  type=float)

  parser.add_argument(
  ‘-e’,
  ‘–epochs’,
  type=int)

  parser.add_argument(
  ‘-b’,
  ‘–batch_size’,
  type=int)

  parser.add_argument(
  ‘-n’,
  ‘–num_workers’,
  type=int)

  parser.add_argument(
  ‘-t’,
  ‘–train_test_split_ratio’,
  type=float)

  parser.add_argument(
  ‘-v’, ‘–validation_split_ratio’, type=float)

  args = parser.parse_args()

  data_dir = args.data_dir

  model_dir = args.model_dir

  if args.learning_rate:

  lr = args.learning_rate

  else:

  lr = None

  if args.batch_size:

  bs = args.batch_size

  else:

  bs = None

  if args.num_workers:

  nw = args.num_workers

  else:

  nw = None

  if args.train_test_split_ratio:

  trsr = args.train_test_split_ratio

  else:

  trsr = None

  if args.validation_split_ratio:

  vsr = args.validation_split_ratio

  else:

  vsr = None

  Read csv file containing sensor readings into pandas dataframe called df_sensor_readings

  Get column names from df_sensor_readings

  Get sensor IDs from column names

  Get list containing time values corresponding each sensor reading

  Get list containing pressure values corresponding each sensor reading

  Get list containing temperature values corresponding each sensor reading

  Create empty lists called time_list

  for storing time values

  of individual sensors

  and pressure_list

  for storing pressure values

  of individual sensors

  for each sensor ID s_id

  in sensor_ids_list

  Create empty lists called temperature_list

  for storing temperature values

  of individual sensors

  for each sensor ID s_id

  in sensor_ids_list

  Initialize variables called t_start_time

  and p_start_time

  with value zero

  Initialize variable called n_sensors

  with value equal length of sensor_ids_list

  Initialize variable called n_samples_per_sensor

  with value equal length divided by n_sensors

  Initialize variable called t_start_index

  with value zero

  Initialize variable called p_start_index

  with value zero

  Initialize variable called temp_start_index

  with value zero

  if __name__ == ‘__main__’:
  main()
  mohamedahmedelmasry/Smart-Water-Pipeline-Monitoring<|file_sep