Annan Athletic Profile: Stats, Performance & Key Players Overview

Overview / Introduction about Annan Athletic

Annan Athletic, based in Annan, Scotland, competes in the Scottish League Two. Founded in 1879, the club is managed by Mark Roberts. Known for their passionate fanbase and historic grounds at Galabank Stadium, they play with a traditional 4-4-2 formation.

Team History and Achievements

Throughout their history, Annan Athletic has experienced notable seasons. They reached the Scottish Cup quarter-finals multiple times in the early 1900s and have been a consistent presence in Scottish football leagues. Their resilience and community support have been hallmarks of their journey.

Current Squad and Key Players

Key Players:
Cameron Kerr – Midfielder known for his vision and passing accuracy.
Danny McKenna – Striker with a knack for scoring crucial goals.
Andrew Murray – Defender providing stability at the back.

Team Playing Style and Tactics

Annan Athletic employs a balanced 4-4-2 formation focusing on solid defense and quick counterattacks. Their strategy leverages strong midfield control to transition into attack efficiently. Strengths include disciplined defense and tactical flexibility, while weaknesses may arise from limited attacking options under pressure.

Interesting Facts and Unique Traits

Annan Athletic is affectionately nicknamed “The Doonhamers,” reflecting their deep community roots. The club’s rivalry with Dumfries adds excitement to league fixtures. Traditions include pre-match chants that resonate with fans’ pride in their heritage.

Frequently Asked Questions (FAQ)

What are Annan Athletic’s recent performances?

Annan Athletic has shown steady performance in League Two, often finishing mid-table with occasional upward surges during key fixtures.

Who are some standout players on the squad?

Cameron Kerr and Danny McKenna have been standout performers due to their contributions both defensively and offensively.

What should bettors consider when betting on Annan Athletic?

Bettors should analyze head-to-head records against opponents, recent form, and any injuries affecting key players to make informed decisions.

Lists & Rankings of Players, Stats, or Performance Metrics

Danny McKenna: Top scorer ✅ High goal conversion rate 💡 Consistent performer ❌ Occasional defensive lapses 🎰
Cameron Kerr: Key playmaker ✅ Excellent passing range 💡 Strong midfield presence ❌ Susceptible to fouls 🎰

Comparisons with Other Teams in the League or Division

Annan Athletic often competes closely with teams like East Fife and Airdrieonians. While they share similar resources, Annan’s strategic adaptability gives them an edge in crucial matches.

Case Studies or Notable Matches

A memorable match was their victory over East Kilbride in the league cup quarter-finals, showcasing their tactical prowess under pressure. This game highlighted Danny McKenna’s critical role as a goal-scorer.

Statistic	Last Season	This Season (to date)
Total Goals Scored	45	22*
Total Goals Conceded	38	19*
Last Five Matches Form (W-D-L)	N/A	3-1-1*

Tips & Recommendations for Analyzing the Team or Betting Insights

Analyze recent head-to-head matchups to identify trends.
Maintain awareness of player injuries that could impact team dynamics.
Evaluate manager strategies against different types of opponents for potential advantages or weaknesses.

“Annan Athletic’s ability to adapt tactically makes them unpredictable opponents.” – Local Football Analysts Association Expert Review.

Pros & Cons of the Team’s Current Form or Performance

Pros:

Here’s the snippet:

python
class NormalInverseGammaDistribution(distributions.Continuous):
def __init__(self,
mu=tf.placeholder(tf.float32),
tau=tf.placeholder(tf.float32),
alpha=tf.placeholder(tf.float32),
beta=tf.placeholder(tf.float32),
gamma=tf.constant([1], dtype=tf.float32),
w=tf.constant([1], dtype=tf.float32),
covariance_matrix = None,
k=None,
name=’NIW_Distribution’):
super(NormalInverseGammaDistribution,self).__init__(
dtype=tf.float32,
param_dtype=tf.float32,
name=name)
self.mu = mu
self.tau = tau
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.w = w
self._covariance_matrix= None
self.k = k
def _batch_shape_tensor(self):

I’m curious about how these classes are supposed to be used effectively within TensorFlow Probability models. Could you help me understand what might be missing here or suggest improvements? Thanks!
<>: python
It looks like you’re working with custom distribution classes using TensorFlow Probability (TFP). Here are a few suggestions and considerations:

1. **Placeholders**: TensorFlow has deprecated placeholders since version 2.x in favor of `tf.function` and eager execution. Consider using `tf.Variable` or directly passing tensors.

2. **Covariance Matrix**: Ensure that `covariance_matrix` is properly initialized if it’s required by your distribution logic.

3. **Batch Shape**: Implement `_batch_shape_tensor()` to return the shape of batches if needed for your model.

4. **Sampling Methods**: Implement methods like `_sample_n()` to allow sampling from your distribution.

5. **Log Probability**: Implement `_log_prob()` to compute log probabilities if you plan to use this distribution within probabilistic models.

6. **Parameter Validation**: Add checks to ensure parameters like `mu`, `tau`, etc., are valid (e.g., positive values where necessary).

7. **Documentation**: Add docstrings to describe each class method for better maintainability.

By addressing these points, you can enhance your custom distributions’ integration within TFP models effectively. 😊

<>: Got it! I’ll start by updating the placeholders since they’re deprecated in TensorFlow 2.x. I’ll switch them to using tf.Variable or directly pass tensors where applicable.

Also, I’ll work on implementing methods like _sample_n() for sampling from these distributions and _log_prob() for computing log probabilities.

Here’s an updated snippet for the NormalInverseGammaDistribution class:

python
class NormalInverseGammaDistribution(distributions.Continuous):
def __init__(self,
mu,
tau,
alpha,
beta,
gamma=tf.constant([1], dtype=tf.float32),
w=tf.constant([1], dtype=tf.float32),
covariance_matrix=None,
k=None,
name=’NIW_Distribution’):
super(NormalInverseGammaDistribution,self).__init__(
dtype=tf.float32,
param_dtype=tf.float32,
name=name)
self.mu = tf.convert_to_tensor(mu)
self.tau = tf.convert_to_tensor(tau)
self.alpha = tf.convert_to_tensor(alpha)
self.beta = tf.convert_to_tensor(beta)
self.gamma = gamma
self.w = w
self._covariance_matrix= covariance_matrix
self.k = k

def _sample_n(self, n, seed=None):
# Implement sampling logic here
pass

def _log_prob(self, value):
# Implement log probability calculation here
pass

def _batch_shape_tensor(self):
# Return batch shape tensor here
pass

Does this look like a good start? Let me know if there’s anything else I should focus on!
<>: Sure! Your updated snippet looks like a solid start towards making your custom distribution compatible with TensorFlow Probability in version 2.x. Here are a few additional pointers:

1. **Sampling Logic (`_sample_n`)**: You’ll need to implement logic that generates samples based on your distribution parameters (`mu`, `tau`, etc.). This typically involves generating samples from underlying distributions (e.g., normal for mean parameters).

2. **Log Probability Calculation (`_log_prob`)**: This method should compute the log probability of given values under your distribution using its mathematical formulation.

3. **Batch Shape (`_batch_shape_tensor`)**: Ensure this method returns the correct batch shape based on input dimensions.

4. **Parameter Constraints**: Consider adding assertions or constraints to ensure parameters meet expected conditions (e.g., positive values).

5. **Testing**: Once implemented, test your class thoroughly with various inputs to ensure correctness.

These steps will help ensure your custom distribution integrates smoothly into TFP models! 😊<>: Hi there! I’m working on an image processing project where I need to generate tiles from images using various techniques like blending modes such as multiply or screen mode overlaying images onto tile backgrounds based on zoom levels (zoom0). Here’s a snippet of my code:

python
# Open this image file.
img0_fullpath = os.path.join(img_dir_fullpath_this,image_file_name_this)
img0_filename_this_png = “.”.join(os.path.basename(img0_fullpath).split(“.”)[:-1]) + “.png”
img0_fullpath_png = os.path.join( png_dir_fullpath_this,img0_filename_this_png )
with Image.open( img0_fullpath ) as img0:
# Get sizes.
img0_size = img0.size # In pixels tuple (width,height).
img0_size_original_pixels_per_meter_w,h = [ s / tile_info_this[units_key][“scale_factor”] for s in img0_size ]
# Create tileinfo list.
tile_info_list += [
# dict(
# image_name =img0_filename_this_png ,
# image_filename=img0_fullpath_png ,
# size=(img_width,img_height) ,
# position=(pos_x,pos_y) ,
# size_pixels_per_meter=(s_w,s_h) ,
# )
dict(
image_name =img0_filename_this_png ,
image_filename=img0_fullpath_png ,
size =(img_width,img_height) , # In pixels.
position =(pos_x,pos_y) , # Upper-left corner coordinate tuple.
size_meters =(s_w,s_h) , # Original image expressed in meters relative-to-satellite.
)
]

I’m feeling a bit confused about how exactly I should handle blending modes when overlaying images onto tile backgrounds at different zoom levels (zoom0). Could you provide some guidance or examples on how best approach this part? Thanks!
<>: python
To handle blending modes effectively when overlaying images onto tile backgrounds at different zoom levels, you can use libraries like PIL/Pillow along with NumPy for more complex operations if needed.

Here’s a basic approach using Pillow:

1. **Load Images:** Open both your base tile image and the overlay image using Pillow.

python
from PIL import Image

base_image_path = ‘path/to/base_tile.png’
overlay_image_path = ‘path/to/overlay.png’

base_image = Image.open(base_image_path).convert(“RGBA”)
overlay_image = Image.open(overlay_image_path).convert(“RGBA”)

2. **Resize Overlay:** Ensure that your overlay image is resized appropriately according to the zoom level.

python
zoom_factor = get_zoom_factor(zoom_level) # Define this function based on your requirements.
new_size_overlay = (int(overlay_image.width * zoom_factor), int(overlay_image.height * zoom_factor))
overlay_image_resized = overlay_image.resize(new_size_overlay)

3. **Apply Blending Mode:** Use Pillow’s blend function or manually apply blending modes using NumPy arrays if more control is needed.

python
blended_image_pillow_method(base_image.paste(overlay_image_resized), blend_mode=’multiply’)

def blended_image_pillow_method(base_img_pasted_img_overlay):
return Image.blend(base_img_pasted_img_overlay[0], base_img_pasted_img_overlay[1], alpha=0.5) # Example alpha value

# For more complex blending:
import numpy as np

def blend_multiply(base_array, overlay_array):
return np.clip(base_array * overlay_array / 255, 0, 255).astype(‘uint8’)

base_array_np = np.array(base_img_pasted_img_overlay[0])
overlay_array_np = np.array(overlay_img_resized)

result_array_np = blend_multiply(base_array_np[…, :3], overlay_array_np[…, :3])
result_image_np_with_alpha=np.dstack((result_array_np,result_array_np[…, -1]))

return Image.fromarray(result_array_np_with_alpha)

final_result_image=blended_image_pillow_method((base_image,paste_position))

4. **Save Result:** Save or display your final blended image.

python
final_result_image.save(‘output_path.png’)

This example uses basic blending via Pillow’s built-in methods but can be extended with NumPy for more sophisticated operations like multiply or screen modes by manipulating pixel arrays directly.

Feel free to adjust blending logic according to specific needs! 😊

<>: Great advice! Now, I want to make sure that when applying these blending modes at different zoom levels (zoom0), each tile background gets correctly processed without losing quality or misaligning overlays due to resolution changes at each level of detail (LOD). How can I dynamically adjust both images before blending so they align perfectly across all LODs? Any tips on handling resolution changes effectively?
<>: To ensure perfect alignment across all levels of detail (LOD) while maintaining quality during blending at different zoom levels (zoom0), consider these strategies:

1. **Consistent Scaling Factor:** Use consistent scaling factors based on zoom levels across all tiles and overlays.

python
def get_zoom_factor(zoom_level):
# Define scaling logic based on zoom level.
return scale_factor_based_on_zoom(zoom_level)

2. **Resolution Management:** Maintain high-resolution source images and scale down as needed rather than scaling up smaller images.

3. **Alignment Check:** Before blending, ensure both images are aligned by calculating offsets based on their positions relative to each other.

4. **Dynamic Resizing:** Dynamically resize both base tiles and overlays according to current LOD while maintaining aspect ratios.

5. **Use Bicubic Resampling:** When resizing images with Pillow, use bicubic resampling for better quality preservation:

python
resized_base_tile = base_tile.resize(new_size_base_tile, Image.BICUBIC)
resized_overlay_tile = overlay_tile.resize(new_size_overlay_tile, Image.BICUBIC)

6. **Pixel Alignment:** Ensure pixel-perfect alignment by rounding calculations during resizing operations:

python
pos_x_aligned = round(pos_x * current_zoom_factor)
pos_y_aligned = round(pos_y * current_zoom_factor)

7. **Quality Checks:** Regularly check output quality at different LODs by comparing against original resolutions visually or through automated tests if possible.

By following these practices, you can maintain high-quality overlays across varying resolutions without misalignment issues.
<>: Awesome tips! Now, how can I dynamically adjust both images before blending so they align perfectly across all LODs? Specifically interested in handling resolution changes effectively without losing quality or causing misalignment due to differing resolutions at each level of detail.
<>: To dynamically adjust images for perfect alignment across all LODs while handling resolution changes effectively:

1. **Unified Coordinate System:** Establish a unified coordinate system where both base tiles and overlays are positioned consistently relative to each other regardless of zoom level.

2. **Calculate Scale Factors Dynamically:** Compute scale factors dynamically based on current LOD:

python
def calculate_scale_factors(current_lod):
# Example logic; adjust according to specific requirements.
return original_scale / current_lod_scale_factor(current_lod)

scale_base_tile = calculate_scale_factors(current_lod)
scale_overlay_tile = calculate_scale_factors(current_lod)

3..**Rescale Both Images Consistently:** Apply calculated scale factors uniformly:

python
resized_base_tile=base_tile.resize((int(base_tile.width*scale_base_tile), int(base_tile.height*scale_base_tile)), Image.BICUBIC)
resized_overlay_tile=overlay.tile.resize((int(overlay.tile.width*scale_overlay_tile), int(overlay.tile.height*scale_overl_tile)), Image.BICUBIC)

4..**Align Using Calculated Offsets:** Calculate offsets after resizing:

offset_x=calculate_offset_x(position_base_tile.position,resized_base_tile.size,resized_overl.tile.size,current_zoom_level))
offset_y=calculate_offset_y(position_base_tile.position,resized_base_til.e.size,resized_overl.tile.size,current_zoom_level))

5..**Apply Offsets Before Blending:** Position overlays accurately before blending:

final_position=(pos_x+offset_x,pos_y+offset_y)

6..**Blend Using Aligned Images:** Proceed with blending once alignment is ensured:

blended_result=blend_images(resized_base_til.e,resized_overl.tile,paste_position=fina_position)

7..**Regular Quality Assurance Checks:** Continuously verify output quality through visual inspection or automated tests across various LODs ensuring consistency without misalignment issues.

By following these steps meticulously you can achieve seamless alignment between tiles across varying resolutions while preserving quality throughout different levels of detail.

<>: Got it! Now, how do I implement dynamic adjustment so both images align perfectly across all LODs? Specifically looking at handling resolution changes without losing quality or causing misalignment due to differing resolutions at each level of detail.
<>: To implement dynamic adjustment ensuring perfect alignment across all LODs while managing resolution changes effectively:

1. **Unified Coordinate System:** Establish coordinates consistently relative between base tiles and overlays regardless of zoom level.

2. **Dynamic Scale Factors Calculation:**

python
def calculate_scale_factors(current_lod):
return original_scale / current_lod_scale_factor(current_lod)

scale_base_tile = calculate_scale_factors(current_lod)
scale_overlay_tile = calculate_scale_factors(current_lod)

3. **Consistent Rescaling Using Bicubic Resampling:**

python
resized_base_tile=base_tile.resize((int(base_tile.width*scale_base_til.e), int(base_til.e.height*scale_basetile)), Image.BICUBIC)
resized_overl.tile=resiz.overlay.tile((int(overl.tile.width*scale_overl.ti.le), int(overl.tile.height*scale_overl.ti.le)),Image.BICUBIC)

4..**Offset Calculation Post-Resizing For Alignment:**

offset_x=calculate_offset_x(position_basetile.position,resiz.ed_basetile.size,resiz.ed_overlaytile.size,current_zoom_level))
offset_y=calculate_offset_y(position_basetile.position,resiz.ed_basetile.size,resiz.ed_overlaytile.size,current_zoom_level))

5..**Position Overlays Accurately Before Blending Using Calculated Offsets:**

final_position=(pos_x+offset_x,pos_y+offset_y)

6..**Proceed With Blending Once Alignment Is Confirmed:**

blended_result=blend_images(resiz.ed_basetile.resiz.ed_overlaytile,paste_position=fina_position)

7..**Conduct Regular Quality Assurance Checks Across Various LODs To Ensure Consistency Without Misalignment Issues.

By following these steps carefully you can achieve seamless alignment between tiles across varying resolutions while maintaining quality throughout different levels of detail.

<>: Alright, let’s dive deeper into calculating those offsets post-resizing for alignment purposes specifically related to my existing code structure involving ’tile_info_list’. How do I integrate offset calculations into my current setup? Also any tips on optimizing performance during these operations?
<>: To integrate offset calculations into your existing setup involving `tile_info_list`, follow these steps:

1. **Extract Current Position Data:** Retrieve position data from `tile_info_list` for both base tiles and overlays.

python
base_pos_x(tile_info[‘position’][0])
base_pos_y(tile_info[‘position’][1])

overlay_pos_x(tile_info[‘position’][0])
overlay_pos_y(tile_info[‘position’][1])

2. **Calculate Offsets After Resizing:**

python
def calculate_offsets(resized_base_size,resized_overlay_size,current_zoom_level):

offset_x=(base_pos_x-overlay_pos_x)*(current_zoom_level/resize.scale_factor)*rescaled_ratio_for_alignment()
offset_y=(base_pos_y-overlay_pos_y)*(current_zoom_level/resize.scale_factor)*rescaled_ratio_for_alignment()

return offset_x+offset_y

offsets=caculate_offsets(reszied_basetile_szie,reszied_ovrelaytile_szie,current_zoomevelo)

3..**Integrate Offset Calculations Into Your Code Structure:**

Update positions before blending using calculated offsets:

final_position=(base_pos.x+offsetx.base_pos.y+offsety)

for tile_info_in_list:
tile_info[‘position’]=(final_poition.x,fina_poition.y)

4..**Optimize Performance Tips During Operations:**

– Use efficient data structures like NumPy arrays for large-scale operations.
– Minimize redundant calculations by caching results where possible.
– Profile code sections using tools like cProfile.pyt hon libraryto identify bottlenecksand optimize them accordingly.

By integrating offset calculations into your existing code structureandfollowingperformance optimizationtips,youcanachieveefficientandaccuratealignmentacrossallLODswhilemaintaininghighqualityoutputs.

Annan Athletic: Premier League South Squad, Achievements & Stats